Add uniform sampling and transition smoothing

* Add generate and validate script

* fix precommit

* Improve generate embeddings function by using dataset tools (#2206)

---------

Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>

.github/workflows/documentation-upload-pr.yml

@@ -31,8 +31,7 @@ jobs:
     name: Upload Preview and Comment
     if: >
       github.event.workflow_run.event == 'pull_request' &&
-      github.event.workflow_run.conclusion == 'success' &&
-      github.repository == 'huggingface/lerobot'
+      github.event.workflow_run.conclusion == 'success'
     uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
     with:
       package_name: lerobot

.github/workflows/documentation.yml

@@ -42,9 +42,7 @@ jobs:
   # This job builds and deploys the official documentation.
   build_main_docs:
     name: Build Main Docs
-    if: >
-      (github.event_name == 'push' || github.event_name == 'workflow_dispatch') &&
-      github.repository == 'huggingface/lerobot'
+    if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
     permissions:
       contents: read
     uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
@@ -60,7 +58,7 @@ jobs:
   # The result of this job triggers the 'Upload PR Documentation' workflow.
   build_pr_docs:
     name: Build PR Docs
-    if: github.event_name == 'pull_request' && github.repository == 'huggingface/lerobot'
+    if: github.event_name == 'pull_request'
     permissions:
       contents: read
       pull-requests: write

.github/workflows/fast_tests.yml

@@ -45,6 +45,7 @@ permissions:
 env:
   UV_VERSION: "0.8.0"
   PYTHON_VERSION: "3.10"
+  DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
 
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.
 concurrency:
@@ -59,19 +60,12 @@ jobs:
     runs-on: ubuntu-latest
     env:
       MUJOCO_GL: egl
-      HF_HOME: /mnt/cache/.cache/huggingface
-      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
     steps:
       - uses: actions/checkout@v4
         with:
           persist-credentials: false
           lfs: true
 
-      # NOTE(Steven): Mount to `/mnt` to avoid the limited storage on `/home`. Consider cleaning default SDKs or using self-hosted runners for more space.
-      # (As of 2024-06-10, the runner's `/home` has only 6.2 GB free—8% of its 72 GB total.)
-      - name: Setup /mnt storage
-        run: sudo chown -R $USER:$USER /mnt
-
       # TODO(Steven): Evaluate the need of these dependencies
       - name: Install apt dependencies
         run: |

.github/workflows/full_tests.yml

@@ -58,19 +58,12 @@ jobs:
       github.event_name == 'workflow_dispatch'
     env:
       MUJOCO_GL: egl
-      HF_HOME: /mnt/cache/.cache/huggingface
-      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
     steps:
       - uses: actions/checkout@v4
         with:
           lfs: true
           persist-credentials: false
 
-      # NOTE(Steven): Mount to `/mnt` to avoid the limited storage on `/home`. Consider cleaning default SDKs or using self-hosted runners for more space.
-      # (As of 2024-06-10, the runner's `/home` has only 6.2 GB free—8% of its 72 GB total.)
-      - name: Setup /mnt storage
-        run: sudo chown -R $USER:$USER /mnt
-
       - name: Install apt dependencies
         run: |
           sudo apt-get update && sudo apt-get install -y build-essential \

.github/workflows/nightly.yml

@@ -43,7 +43,6 @@ jobs:
     name: Build CPU Docker for Nightly
     runs-on:
       group: aws-general-8-plus
-    if: github.repository == 'huggingface/lerobot'
     outputs:
       image_tag: ${{ env.DOCKER_IMAGE_NAME_CPU }}
     steps:
@@ -78,7 +77,6 @@ jobs:
     name: Build GPU Docker for Nightly
     runs-on:
       group: aws-general-8-plus
-    if: github.repository == 'huggingface/lerobot'
     outputs:
       image_tag: ${{ env.DOCKER_IMAGE_NAME_GPU }}
     steps:

.github/workflows/release.yml

@@ -29,7 +29,6 @@ jobs:
   build-and-publish:
     name: Build and publish Python distributions
     runs-on: ubuntu-latest
-    if: github.repository == 'huggingface/lerobot'
     outputs:
       version: ${{ steps.extract_info.outputs.tag_version }}
     permissions:

.github/workflows/stale.yml

@@ -45,7 +45,6 @@ jobs:
   stale:
     name: Close Stale Issues and PRs
     runs-on: ubuntu-latest
-    if: github.repository == 'huggingface/lerobot'
     permissions:
       actions: write
       contents: write # only for delete-branch option

.github/workflows/unbound_deps_tests.yml

@@ -43,22 +43,14 @@ jobs:
   full-tests:
     name: Full Unbound Tests
     runs-on: ubuntu-latest
-    if: github.repository == 'huggingface/lerobot'
     env:
       MUJOCO_GL: egl
-      HF_HOME: /mnt/cache/.cache/huggingface
-      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
     steps:
       - uses: actions/checkout@v4
         with:
           lfs: true
           persist-credentials: false
 
-      # NOTE(Steven): Mount to `/mnt` to avoid the limited storage on `/home`. Consider cleaning default SDKs or using self-hosted runners for more space.
-      # (As of 2024-06-10, the runner's `/home` has only 6.2 GB free—8% of its 72 GB total.)
-      - name: Setup /mnt storage
-        run: sudo chown -R $USER:$USER /mnt
-
       - name: Install apt dependencies
         run: |
           sudo apt-get update && sudo apt-get install -y build-essential \

benchmarks/video/benchmark.py

@@ -0,0 +1,94 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import threading
+import time
+from contextlib import ContextDecorator
+
+
+class TimeBenchmark(ContextDecorator):
+    """
+    Measures execution time using a context manager or decorator.
+
+    This class supports both context manager and decorator usage, and is thread-safe for multithreaded
+    environments.
+
+    Args:
+        print: If True, prints the elapsed time upon exiting the context or completing the function. Defaults
+        to False.
+
+    Examples:
+
+        Using as a context manager:
+
+        >>> benchmark = TimeBenchmark()
+        >>> with benchmark:
+        ...     time.sleep(1)
+        >>> print(f"Block took {benchmark.result:.4f} seconds")
+        Block took approximately 1.0000 seconds
+
+        Using with multithreading:
+
+        ```python
+        import threading
+
+        benchmark = TimeBenchmark()
+
+
+        def context_manager_example():
+            with benchmark:
+                time.sleep(0.01)
+            print(f"Block took {benchmark.result_ms:.2f} milliseconds")
+
+
+        threads = []
+        for _ in range(3):
+            t1 = threading.Thread(target=context_manager_example)
+            threads.append(t1)
+
+        for t in threads:
+            t.start()
+
+        for t in threads:
+            t.join()
+        ```
+        Expected output:
+        Block took approximately 10.00 milliseconds
+        Block took approximately 10.00 milliseconds
+        Block took approximately 10.00 milliseconds
+    """
+
+    def __init__(self, print=False):
+        self.local = threading.local()
+        self.print_time = print
+
+    def __enter__(self):
+        self.local.start_time = time.perf_counter()
+        return self
+
+    def __exit__(self, *exc):
+        self.local.end_time = time.perf_counter()
+        self.local.elapsed_time = self.local.end_time - self.local.start_time
+        if self.print_time:
+            print(f"Elapsed time: {self.local.elapsed_time:.4f} seconds")
+        return False
+
+    @property
+    def result(self):
+        return getattr(self.local, "elapsed_time", None)
+
+    @property
+    def result_ms(self):
+        return self.result * 1e3

benchmarks/video/capture_camera_feed.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Capture video feed from a camera as raw images."""
+
+import argparse
+import datetime as dt
+import os
+import time
+from pathlib import Path
+
+import cv2
+import rerun as rr
+
+# see https://rerun.io/docs/howto/visualization/limit-ram
+RERUN_MEMORY_LIMIT = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "5%")
+
+
+def display_and_save_video_stream(output_dir: Path, fps: int, width: int, height: int, duration: int):
+    rr.init("lerobot_capture_camera_feed")
+    rr.spawn(memory_limit=RERUN_MEMORY_LIMIT)
+
+    now = dt.datetime.now()
+    capture_dir = output_dir / f"{now:%Y-%m-%d}" / f"{now:%H-%M-%S}"
+    if not capture_dir.exists():
+        capture_dir.mkdir(parents=True, exist_ok=True)
+
+    # Opens the default webcam
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        print("Error: Could not open video stream.")
+        return
+
+    cap.set(cv2.CAP_PROP_FPS, fps)
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
+
+    frame_index = 0
+    start_time = time.time()
+    while time.time() - start_time < duration:
+        ret, frame = cap.read()
+
+        if not ret:
+            print("Error: Could not read frame.")
+            break
+        rr.log("video/stream", rr.Image(frame), static=True)
+        cv2.imwrite(str(capture_dir / f"frame_{frame_index:06d}.png"), frame)
+        frame_index += 1
+
+    # Release the capture
+    cap.release()
+
+    # TODO(Steven): Add a graceful shutdown via a close() method for the Viewer context, though not currently supported in the Rerun API.
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("outputs/cam_capture/"),
+        help="Directory where the capture images are written. A subfolder named with the current date & time will be created inside it for each capture.",
+    )
+    parser.add_argument(
+        "--fps",
+        type=int,
+        default=30,
+        help="Frames Per Second of the capture.",
+    )
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=1280,
+        help="Width of the captured images.",
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=720,
+        help="Height of the captured images.",
+    )
+    parser.add_argument(
+        "--duration",
+        type=int,
+        default=20,
+        help="Duration in seconds for which the video stream should be captured.",
+    )
+    args = parser.parse_args()
+    display_and_save_video_stream(**vars(args))

benchmarks/video/run_video_benchmark.py

@@ -21,13 +21,11 @@ See the provided README.md or run `python benchmark/video/run_video_benchmark.py
 
 import argparse
 import datetime as dt
-import itertools
 import random
 import shutil
 from collections import OrderedDict
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
-from threading import Lock
 
 import einops
 import numpy as np
@@ -37,13 +35,13 @@ import torch
 from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
 from tqdm import tqdm
 
+from benchmarks.video.benchmark import TimeBenchmark
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.video_utils import (
-    decode_video_frames,
+    decode_video_frames_torchvision,
     encode_video_frames,
 )
 from lerobot.utils.constants import OBS_IMAGE
-from lerobot.utils.utils import TimerManager
 
 BASE_ENCODING = OrderedDict(
     [
@@ -88,7 +86,7 @@ def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> t
     frames = []
     for ts in timestamps:
         idx = int(ts * fps)
-        frame = PIL.Image.open(imgs_dir / f"frame-{idx:06d}.png")
+        frame = PIL.Image.open(imgs_dir / f"frame_{idx:06d}.png")
         frame = torch.from_numpy(np.array(frame))
         frame = frame.type(torch.float32) / 255
         frame = einops.rearrange(frame, "h w c -> c h w")
@@ -99,21 +97,21 @@ def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> t
 def save_decoded_frames(
     imgs_dir: Path, save_dir: Path, frames: torch.Tensor, timestamps: list[float], fps: int
 ) -> None:
-    if save_dir.exists() and len(list(save_dir.glob("frame-*.png"))) == len(timestamps):
+    if save_dir.exists() and len(list(save_dir.glob("frame_*.png"))) == len(timestamps):
         return
 
     save_dir.mkdir(parents=True, exist_ok=True)
     for i, ts in enumerate(timestamps):
         idx = int(ts * fps)
         frame_hwc = (frames[i].permute((1, 2, 0)) * 255).type(torch.uint8).cpu().numpy()
-        PIL.Image.fromarray(frame_hwc).save(save_dir / f"frame-{idx:06d}_decoded.png")
-        shutil.copyfile(imgs_dir / f"frame-{idx:06d}.png", save_dir / f"frame-{idx:06d}_original.png")
+        PIL.Image.fromarray(frame_hwc).save(save_dir / f"frame_{idx:06d}_decoded.png")
+        shutil.copyfile(imgs_dir / f"frame_{idx:06d}.png", save_dir / f"frame_{idx:06d}_original.png")
 
 
 def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
     episode_index = 0
     ep_num_images = dataset.meta.episodes["length"][episode_index]
-    if imgs_dir.exists() and len(list(imgs_dir.glob("frame-*.png"))) == ep_num_images:
+    if imgs_dir.exists() and len(list(imgs_dir.glob("frame_*.png"))) == ep_num_images:
         return
 
     imgs_dir.mkdir(parents=True, exist_ok=True)
@@ -127,7 +125,7 @@ def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
         tqdm(imgs_dataset, desc=f"saving {dataset.repo_id} first episode images", leave=False)
     ):
         img = item[img_keys[0]]
-        img.save(str(imgs_dir / f"frame-{i:06d}.png"), quality=100)
+        img.save(str(imgs_dir / f"frame_{i:06d}.png"), quality=100)
 
         if i >= ep_num_images - 1:
             break
@@ -151,6 +149,18 @@ def sample_timestamps(timestamps_mode: str, ep_num_images: int, fps: int) -> lis
     return [idx / fps for idx in frame_indexes]
 
 
+def decode_video_frames(
+    video_path: str,
+    timestamps: list[float],
+    tolerance_s: float,
+    backend: str,
+) -> torch.Tensor:
+    if backend in ["pyav", "video_reader"]:
+        return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
+    else:
+        raise NotImplementedError(backend)
+
+
 def benchmark_decoding(
     imgs_dir: Path,
     video_path: Path,
@@ -162,8 +172,8 @@ def benchmark_decoding(
     num_workers: int = 4,
     save_frames: bool = False,
 ) -> dict:
-    def process_sample(sample: int, lock: Lock):
-        time_benchmark = TimerManager(log=False)
+    def process_sample(sample: int):
+        time_benchmark = TimeBenchmark()
         timestamps = sample_timestamps(timestamps_mode, ep_num_images, fps)
         num_frames = len(timestamps)
         result = {
@@ -172,13 +182,13 @@ def benchmark_decoding(
             "mse_values": [],
         }
 
-        with time_benchmark, lock:
+        with time_benchmark:
             frames = decode_video_frames(video_path, timestamps=timestamps, tolerance_s=5e-1, backend=backend)
-        result["load_time_video_ms"] = (time_benchmark.last * 1000) / num_frames
+        result["load_time_video_ms"] = time_benchmark.result_ms / num_frames
 
         with time_benchmark:
             original_frames = load_original_frames(imgs_dir, timestamps, fps)
-        result["load_time_images_ms"] = (time_benchmark.last * 1000) / num_frames
+        result["load_time_images_ms"] = time_benchmark.result_ms / num_frames
 
         frames_np, original_frames_np = frames.numpy(), original_frames.numpy()
         for i in range(num_frames):
@@ -205,10 +215,8 @@ def benchmark_decoding(
     # A sample is a single set of decoded frames specified by timestamps_mode (e.g. a single frame, 2 frames, etc.).
     # For each sample, we record metrics (loading time and quality metrics) which are then averaged over all samples.
     # As these samples are independent, we run them in parallel threads to speed up the benchmark.
-    # Use a single shared lock for all worker threads
-    shared_lock = Lock()
     with ThreadPoolExecutor(max_workers=num_workers) as executor:
-        futures = [executor.submit(process_sample, i, shared_lock) for i in range(num_samples)]
+        futures = [executor.submit(process_sample, i) for i in range(num_samples)]
         for future in tqdm(as_completed(futures), total=num_samples, desc="samples", leave=False):
             result = future.result()
             load_times_video_ms.append(result["load_time_video_ms"])
@@ -350,27 +358,24 @@ def main(
                 imgs_dir = output_dir / "images" / dataset.repo_id.replace("/", "_")
                 # We only use the first episode
                 save_first_episode(imgs_dir, dataset)
-                for duet in [
-                    dict(zip(encoding_benchmarks.keys(), unique_combination, strict=False))
-                    for unique_combination in itertools.product(*encoding_benchmarks.values())
-                ]:
-                    encoding_cfg = BASE_ENCODING.copy()
-                    encoding_cfg["vcodec"] = video_codec
-                    encoding_cfg["pix_fmt"] = pixel_format
-                    for key, value in duet.items():
+                for key, values in tqdm(encoding_benchmarks.items(), desc="encodings (g, crf)", leave=False):
+                    for value in tqdm(values, desc=f"encodings ({key})", leave=False):
+                        encoding_cfg = BASE_ENCODING.copy()
+                        encoding_cfg["vcodec"] = video_codec
+                        encoding_cfg["pix_fmt"] = pixel_format
                         encoding_cfg[key] = value
-                    args_path = Path("_".join(str(value) for value in encoding_cfg.values()))
-                    video_path = output_dir / "videos" / args_path / f"{repo_id.replace('/', '_')}.mp4"
-                    benchmark_table += benchmark_encoding_decoding(
-                        dataset,
-                        video_path,
-                        imgs_dir,
-                        encoding_cfg,
-                        decoding_benchmarks,
-                        num_samples,
-                        num_workers,
-                        save_frames,
-                    )
+                        args_path = Path("_".join(str(value) for value in encoding_cfg.values()))
+                        video_path = output_dir / "videos" / args_path / f"{repo_id.replace('/', '_')}.mp4"
+                        benchmark_table += benchmark_encoding_decoding(
+                            dataset,
+                            video_path,
+                            imgs_dir,
+                            encoding_cfg,
+                            decoding_benchmarks,
+                            num_samples,
+                            num_workers,
+                            save_frames,
+                        )
 
             # Save intermediate results
             benchmark_df = pd.DataFrame(benchmark_table, columns=headers)
@@ -404,9 +409,9 @@ if __name__ == "__main__":
         nargs="*",
         default=[
             "lerobot/pusht_image",
-            "lerobot/aloha_mobile_shrimp_image",
-            "lerobot/paris_street",
-            "lerobot/kitchen",
+            "aliberts/aloha_mobile_shrimp_image",
+            "aliberts/paris_street",
+            "aliberts/kitchen",
         ],
         help="Datasets repo-ids to test against. First episodes only are used. Must be images.",
     )
@@ -414,7 +419,7 @@ if __name__ == "__main__":
         "--vcodec",
         type=str,
         nargs="*",
-        default=["h264", "hevc", "libsvtav1"],
+        default=["libx264", "hevc", "libsvtav1"],
         help="Video codecs to be tested",
     )
     parser.add_argument(
@@ -463,7 +468,7 @@ if __name__ == "__main__":
         "--backends",
         type=str,
         nargs="*",
-        default=["torchcodec", "pyav"],
+        default=["pyav", "video_reader"],
         help="Torchvision decoding backend to be tested.",
     )
     parser.add_argument(

docs/source/_toctree.yml

@@ -9,8 +9,6 @@
     title: Imitation Learning for Robots
   - local: cameras
     title: Cameras
-  - local: bring_your_own_policies
-    title: Bring Your Own Policies
   - local: integrate_hardware
     title: Bring Your Own Hardware
   - local: hilserl
@@ -39,8 +37,6 @@
     title: π₀.₅ (Pi05)
   - local: groot
     title: NVIDIA GR00T N1.5
-  - local: xvla
-    title: X-VLA
   title: "Policies"
 - sections:
   - local: async
@@ -51,8 +47,8 @@
 - sections:
   - local: envhub
     title: Environments from the Hub
-  - local: envhub_leisaac
-    title: Control & Train Robots in Sim (LeIsaac)
+  - local: il_sim
+    title: Imitation Learning in Sim
   - local: libero
     title: Using Libero
   - local: metaworld
@@ -83,19 +79,11 @@
     title: Hope Jr
   - local: reachy2
     title: Reachy 2
-  - local: unitree_g1
-    title: Unitree G1
-  - local: earthrover_mini_plus
-    title: Earth Rover Mini
   title: "Robots"
 - sections:
   - local: phone_teleop
     title: Phone
   title: "Teleoperators"
-- sections:
-  - local: torch_accelerators
-    title: PyTorch accelerators
-  title: "Supported Hardware"
 - sections:
   - local: notebooks
     title: Notebooks

docs/source/async.mdx

@@ -196,7 +196,7 @@ client_cfg = RobotClientConfig(
     server_address="localhost:8080",
     policy_device="mps",
     policy_type="smolvla",
-    pretrained_name_or_path="<user>/smolvla_async",
+    pretrained_name_or_path="fracapuano/smolvla_async",
     chunk_size_threshold=0.5,
     actions_per_chunk=50,  # make sure this is less than the max actions of the policy
 )
@@ -278,7 +278,7 @@ We found the default values of `actions_per_chunk` and `chunk_size_threshold` to
 2. **Adjust your `fps` based on inference latency.** While the server generates a new action chunk, the client is not idle and is stepping through its current action queue. If the two processes happen at fundamentally different speeds, the client might end up with an empty queue. As such, you should reduce your fps if you consistently run out of actions in queue.
 3. **Adjust `chunk_size_threshold`**.
    - Values closer to `0.0` result in almost sequential behavior. Values closer to `1.0` → send observation every step (more bandwidth, relies on good world-model).
-   - We found values around 0.5-0.6 to work well. If you want to tweak this, spin up a `RobotClient` setting the `--debug_visualize_queue_size` to `True`. This will plot the action queue size evolution at runtime, and you can use it to find the value of `chunk_size_threshold` that works best for your setup.
+   - We found values around 0.5-0.6 to work well. If you want to tweak this, spin up a `RobotClient` setting the `--debug-visualize-queue-size` to `True`. This will plot the action queue size evolution at runtime, and you can use it to find the value of `chunk_size_threshold` that works best for your setup.
 
 <p align="center">
   <img
@@ -289,7 +289,7 @@ We found the default values of `actions_per_chunk` and `chunk_size_threshold` to
 <p align="center">
   <i>
     The action queue size is plotted at runtime when the
-    `--debug_visualize_queue_size` flag is passed, for various levels of
+    `--debug-visualize-queue-size` flag is passed, for various levels of
     `chunk_size_threshold` (`g` in the SmolVLA paper).
   </i>
 </p>

docs/source/bring_your_own_policies.mdx

@@ -1,175 +0,0 @@
-# Bring Your Own Policies
-
-This tutorial explains how to integrate your own custom policy implementations into the LeRobot ecosystem, allowing you to leverage all LeRobot tools for training, evaluation, and deployment while using your own algorithms.
-
-## Step 1: Create a Policy Package
-
-Your custom policy should be organized as an installable Python package following LeRobot's plugin conventions.
-
-### Package Structure
-
-Create a package with the prefix `lerobot_policy_` (IMPORTANT!) followed by your policy name:
-
-```bash
-lerobot_policy_my_custom_policy/
-├── pyproject.toml
-└── src/
-    └── lerobot_policy_my_custom_policy/
-        ├── __init__.py
-        ├── configuration_my_custom_policy.py
-        ├── modeling_my_custom_policy.py
-        └── processor_my_custom_policy.py
-```
-
-### Package Configuration
-
-Set up your `pyproject.toml`:
-
-```toml
-[project]
-name = "lerobot_policy_my_custom_policy"
-version = "0.1.0"
-dependencies = [
-    # your policy-specific dependencies
-]
-requires-python = ">= 3.11"
-
-[build-system]
-build-backend = # your-build-backend
-requires = # your-build-system
-```
-
-## Step 2: Define the Policy Configuration
-
-Create a configuration class that inherits from `PreTrainedConfig` and registers your policy type:
-
-```python
-# configuration_my_custom_policy.py
-from dataclasses import dataclass, field
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import NormalizationMode
-
-@PreTrainedConfig.register_subclass("my_custom_policy")
-@dataclass
-class MyCustomPolicyConfig(PreTrainedConfig):
-    """Configuration class for MyCustomPolicy.
-
-    Args:
-        n_obs_steps: Number of observation steps to use as input
-        horizon: Action prediction horizon
-        n_action_steps: Number of action steps to execute
-        hidden_dim: Hidden dimension for the policy network
-        # Add your policy-specific parameters here
-    """
-    # ...PreTrainedConfig fields...
-    pass
-
-    def __post_init__(self):
-        super().__post_init__()
-        # Add any validation logic here
-
-    def validate_features(self) -> None:
-        """Validate input/output feature compatibility."""
-        # Implement validation logic for your policy's requirements
-        pass
-```
-
-## Step 3: Implement the Policy Class
-
-Create your policy implementation by inheriting from LeRobot's base `PreTrainedPolicy` class:
-
-```python
-# modeling_my_custom_policy.py
-import torch
-import torch.nn as nn
-from typing import Dict, Any
-
-from lerobot.policies.pretrained import PreTrainedPolicy
-from .configuration_my_custom_policy import MyCustomPolicyConfig
-
-class MyCustomPolicy(PreTrainedPolicy):
-    config_class = MyCustomPolicyConfig
-    name = "my_custom_policy"
-
-    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: Dict[str, Any] = None):
-        super().__init__(config, dataset_stats)
-        ...
-```
-
-## Step 4: Add Data Processors
-
-Create processor functions:
-
-```python
-# processor_my_custom_policy.py
-from typing import Dict, Any
-import torch
-
-
-def make_my_custom_policy_pre_post_processors(
-    config,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    """Create preprocessing and postprocessing functions for your policy."""
-    pass  # Define your preprocessing and postprocessing logic here
-
-```
-
-## Step 5: Package Initialization
-
-Expose your classes in the package's `__init__.py`:
-
-```python
-# __init__.py
-"""Custom policy package for LeRobot."""
-
-try:
-    import lerobot  # noqa: F401
-except ImportError:
-    raise ImportError(
-        "lerobot is not installed. Please install lerobot to use this policy package."
-    )
-
-from .configuration_my_custom_policy import MyCustomPolicyConfig
-from .modeling_my_custom_policy import MyCustomPolicy
-from .processor_my_custom_policy import make_my_custom_policy_pre_post_processors
-
-__all__ = [
-    "MyCustomPolicyConfig",
-    "MyCustomPolicy",
-    "make_my_custom_policy_pre_post_processors",
-]
-```
-
-## Step 6: Installation and Usage
-
-### Install Your Policy Package
-
-```bash
-cd lerobot_policy_my_custom_policy
-pip install -e .
-
-# Or install from PyPI if published
-pip install lerobot_policy_my_custom_policy
-```
-
-### Use Your Policy
-
-Once installed, your policy automatically integrates with LeRobot's training and evaluation tools:
-
-```bash
-lerobot-train \
-    --policy.type my_custom_policy \
-    --env.type pusht \
-    --steps 200000
-```
-
-## Examples and Community Contributions
-
-Check out these example policy implementations:
-
-- [DiTFlow Policy](https://github.com/danielsanjosepro/lerobot_policy_ditflow) - Diffusion Transformer policy with flow-matching objective. Try it out in this example: [DiTFlow Example](https://github.com/danielsanjosepro/test_lerobot_policy_ditflow)
-
-Share your policy implementations with the community! 🤗

docs/source/earthrover_mini_plus.mdx

@@ -1,206 +0,0 @@
-# EarthRover Mini Plus
-
-The EarthRover Mini Plus is a fully open source mobile robot that connects through the cloud using the Frodobots SDK. This lets you control the robot and record datasets for training AI models.
-
-## What You Need
-
-### Hardware
-
-- EarthRover Mini robot
-- Computer with Python 3.10 or newer
-- Internet connection
-
-### Setting Up the Frodobots SDK
-
-The robot needs the [Frodobots SDK](https://github.com/Frodobots/earth-rovers-sdk) running on your computer. Here's how:
-
-1. Download and install the SDK:
-
-```bash
-git clone https://github.com/Frodobots/earth-rovers-sdk.git
-cd earth-rovers-sdk
-pip install -r requirements.txt
-```
-
-2. Start the SDK:
-
-```bash
-hypercorn main:app --reload
-```
-
-3. Open your web browser and go to `http://localhost:8000`, then click "Join"
-
-The SDK gives you:
-
-- Live video from front and rear cameras
-
-> [!IMPORTANT]
-> The SDK must be running before you can use the robot.
-
-## Install LeRobot
-
-Follow our [Installation Guide](./installation) to install LeRobot.
-
-In addition to the base installation, install the EarthRover Mini dependencies:
-
-```bash
-pip install -e .
-```
-
-## How It Works
-
-The robot uses the internet to communicate:
-
-- **Movement commands**: Sent through the SDK
-- **Camera video**: Received from the SDK
-- **Robot info**: Battery, location, speed from the SDK
-
-You don't need to plug anything in - it all works through the SDK.
-
-## Calibration
-
-No calibration needed! The robot is ready to use as soon as the SDK is running.
-
-## Controlling the Robot
-
-You control the robot using your keyboard - just like playing a video game with WASD keys.
-
-### Keyboard Controls
-
-| Key | Action                           |
-| --- | -------------------------------- |
-| W   | Move forward                     |
-| S   | Move backward                    |
-| A   | Turn left (with forward motion)  |
-| D   | Turn right (with forward motion) |
-| Q   | Rotate left in place             |
-| E   | Rotate right in place            |
-| X   | Stop all movement                |
-| +/= | Increase speed                   |
-| -   | Decrease speed                   |
-| ESC | Disconnect                       |
-
-### Speed Settings
-
-You can adjust how fast the robot moves:
-
-- **Forward/backward speed**: Default is full speed (1.0)
-- **Turning speed**: Default is full speed (1.0)
-- **Speed changes**: Use +/- keys to adjust by 0.1 each time
-
-### Try It Out
-
-Test driving the robot before recording data:
-
-```python
-from lerobot.robots.earthrover_mini_plus import EarthRoverMiniPlus, EarthRoverMiniPlusConfig
-from lerobot.teleoperators.keyboard import KeyboardRoverTeleop, KeyboardRoverTeleopConfig
-
-# Initialize robot
-robot_config = EarthRoverMiniPlusConfig()
-robot = EarthRoverMiniPlus(robot_config)
-
-# Initialize teleoperator
-teleop_config = KeyboardRoverTeleopConfig(
-    linear_speed=1.0,
-    angular_speed=1.0,
-    speed_increment=0.1
-)
-teleop = KeyboardRoverTeleop(teleop_config)
-
-# Connect
-robot.connect()
-teleop.connect()
-
-# Teleoperate (use keyboard controls)
-try:
-    while True:
-        action = teleop.get_action()
-        robot.send_action(action)
-except KeyboardInterrupt:
-    pass
-finally:
-    robot.disconnect()
-    teleop.disconnect()
-```
-
-> [!TIP]
-> If you're using a Mac, you might need to give Terminal permission to access your keyboard for teleoperation. Go to System Preferences > Security & Privacy > Input Monitoring and check the box for Terminal.
-
-## Recording Data
-
-Once you can drive the robot well, you can start recording data to train AI models. The system records:
-
-- **What you do**: How you move the robot (forward, backward, turning)
-- **What the robot sees**:
-  - Videos from both cameras
-  - Robot speed and direction
-  - Battery level and location
-  - GPS position and signal
-  - Other sensor data
-- **When it happened**: Timestamps for everything
-
-### Setting Up Hugging Face
-
-We use Hugging Face to store your data online. First, log in with your token from [Hugging Face settings](https://huggingface.co/settings/tokens):
-
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Store your Hugging Face username:
-
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-
-### Start Recording
-
-Use the standard recording command:
-
-```bash
-python src/lerobot/scripts/lerobot_record.py \
-    --robot.type=earthrover_mini_plus \
-    --teleop.type=keyboard_rover \
-    --dataset.repo_id=your_username/dataset_name \
-    --dataset.num_episodes=2 \
-    --dataset.fps=10 \
-    --dataset.single_task="Navigate around obstacles" \
-    --display_data=true
-```
-
-Replace `your_username/dataset_name` with your Hugging Face username and a name for your dataset.
-
-### What Gets Saved
-
-Your dataset includes:
-
-**Your Actions (2 things)**:
-
-- How much you moved forward/backward
-- How much you turned left/right
-
-**Robot Observations (12 things)**:
-
-- Front camera video
-- Rear camera video
-- Current speed
-- Battery level
-- Which way the robot is facing
-- GPS location (latitude, longitude, signal strength)
-- Network signal strength
-- Vibration level
-- Lamp status (on/off)
-
-### Where Your Data Goes
-
-On your computer: `~/.cache/huggingface/lerobot/{repo-id}`
-
-After recording, your data automatically uploads to your Hugging Face page:
-
-```bash
-echo https://huggingface.co/datasets/${HF_USER}/earthrover-navigation
-```
-
-Your dataset will be tagged with `LeRobot` for community discovery.

docs/source/envhub_leisaac.mdx

@@ -1,301 +0,0 @@
-# LeIsaac × LeRobot EnvHub
-
-LeRobot EnvHub now supports **imitation learning in simulation** with LeIsaac.
-Spin up everyday manipulation tasks, teleoperate the robot, collect demos, push them to the Hub, and train policies in LeRobot — all in one loop.
-
-[LeIsaac](https://github.com/LightwheelAI/leisaac) integrates with IsaacLab and the SO101 Leader/Follower setup to provide:
-
-- 🕹️ **Teleoperation-first workflows** for data collection
-- 📦 **Built-in data conversion** ready for LeRobot training
-- 🤖 **Everyday skills** like picking oranges, lifting cubes, cleaning tables, and folding cloth
-- ☁️ **Ongoing upgrades** from [LightWheel](https://lightwheel.ai/): cloud simulation, EnvHub support, Sim2Real tooling, and more
-
-Below you’ll find the currently supported LeIsaac tasks exposed through LeRobot EnvHub.
-
-# Available Environments
-
-The following table lists all available tasks and environments in LeIsaac x LeRobot Envhub. You can also get the latest list of environments by running the following command:
-
-```bash
-python scripts/environments/list_envs.py
-```
-
-| Task                                                                                                                                                            | Environment ID                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | Task Description                                                                                                           | Related Robot                                              |
-| :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------- |
-| <video src="https://github.com/user-attachments/assets/466eddff-f720-4f99-94d5-5e123e4c302c" autoplay loop muted playsinline style="max-width: 300px;"></video> | [LeIsaac-SO101-PickOrange-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/pick_orange/pick_orange_env_cfg.py)<br /><br />[LeIsaac-SO101-PickOrange-Direct-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/pick_orange/direct/pick_orange_env.py)                                                                                                                                                                                                                        | Pick three oranges and put them into the plate, then reset the arm to rest state.                                          | Single-Arm SO101 Follower                                  |
-| <video src="https://github.com/user-attachments/assets/1e4eb83a-0b38-40fb-a0b2-ddb0fe201e6d" autoplay loop muted playsinline style="max-width: 300px;"></video> | [LeIsaac-SO101-LiftCube-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/lift_cube/lift_cube_env_cfg.py)<br /><br />[LeIsaac-SO101-LiftCube-Direct-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/lift_cube/direct/lift_cube_env.py)                                                                                                                                                                                                                                    | Lift the red cube up.                                                                                                      | Single-Arm SO101 Follower                                  |
-| <video src="https://github.com/user-attachments/assets/e49d8f1c-dcc9-412b-a88f-100680d8a45b" autoplay loop muted playsinline style="max-width: 300px;"></video> | [LeIsaac-SO101-CleanToyTable-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/clean_toy_table/clean_toy_table_env_cfg.py)<br /><br />[LeIsaac-SO101-CleanToyTable-BiArm-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/clean_toy_table/clean_toy_table_bi_arm_env_cfg.py)<br /><br />[LeIsaac-SO101-CleanToyTable-BiArm-Direct-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/clean_toy_table/direct/clean_toy_table_bi_arm_env.py) | Pick two letter e objects into the box, and reset the arm to rest state.                                                   | Single-Arm SO101 Follower<br /><br />Bi-Arm SO101 Follower |
-| <video src="https://github.com/user-attachments/assets/e29a0f8a-9286-4ce6-b45d-342c3d3ba754" autoplay loop muted playsinline style="max-width: 300px;"></video> | [LeIsaac-SO101-FoldCloth-BiArm-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/fold_cloth/fold_cloth_bi_arm_env_cfg.py)<br /><br />[LeIsaac-SO101-FoldCloth-BiArm-Direct-v0](https://github.com/LightwheelAI/leisaac/blob/main/source/leisaac/leisaac/tasks/fold_cloth/direct/fold_cloth_bi_arm_env.py)                                                                                                                                                                                                    | Fold the cloth, and reset the arm to rest state.<br /><br />_Note: Only the DirectEnv support check_success in this task._ | Bi-Arm SO101 Follower                                      |
-
-# Load LeIsaac directly in LeRobot with one line of code
-
-> EnvHub: Share LeIsaac environments through HuggingFace
-
-[EnvHub](https://huggingface.co/docs/lerobot/envhub) is our reproducible environment hub, spin up a packaged simulation with one line, experiment immediately, and publish your own tasks for the community.
-
-LeIsaac offers EnvHub support so you can consume or share tasks with only a few commands.
-
-<video
-  controls
-  src="https://github.com/user-attachments/assets/687666f5-ebe0-421d-84a0-eb86116ac5f8"
-  style={{ width: "100%", maxWidth: "960px", borderRadius: "8px" }}
-/>
-
-## How to get started, environment Setup
-
-Run the following commands to setup your code environments:
-
-```bash
-# Refer to Getting Started/Installation to install leisaac firstly
-conda create -n leisaac_envhub python=3.11
-conda activate leisaac_envhub
-
-conda install -c "nvidia/label/cuda-12.8.1" cuda-toolkit
-pip install -U torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128
-pip install 'leisaac[isaaclab] @ git+https://github.com/LightwheelAI/leisaac.git#subdirectory=source/leisaac' --extra-index-url https://pypi.nvidia.com
-
-# Install lerobot
-pip install lerobot==0.4.1
-
-# Fix numpy version
-pip install numpy==1.26.0
-```
-
-## Usage Example
-
-EnvHub exposes every LeIsaac-supported task in a uniform interface. The examples below load `so101_pick_orange` and demonstrate a random-action rollout and an interactive teleoperation.
-
-### Random Action
-
-<details>
-<summary>Click to expand code example</summary>
-
-```python
-# envhub_random_action.py
-
-import torch
-from lerobot.envs.factory import make_env
-
-# Load from the hub
-envs_dict = make_env("LightwheelAI/leisaac_env:envs/so101_pick_orange.py", n_envs=1, trust_remote_code=True)
-
-# Access the environment
-suite_name = next(iter(envs_dict))
-sync_vector_env = envs_dict[suite_name][0]
-# retrieve the isaac environment from the sync vector env
-env = sync_vector_env.envs[0].unwrapped
-
-# Use it like any gym environment
-obs, info = env.reset()
-
-while True:
-    action = torch.tensor(env.action_space.sample())
-    obs, reward, terminated, truncated, info = env.step(action)
-    if terminated or truncated:
-        obs, info = env.reset()
-
-env.close()
-```
-
-</details>
-
-```bash
-python envhub_random_action.py
-```
-
-You should see the SO101 arm swinging under purely random commands.
-
-### Teleoperation
-
-LeRobot’s teleoperation stack can drive the simulated arm.
-
-Connect the SO101 Leader controller, run the calibration command below.
-
-```bash
-lerobot-calibrate \
-    --teleop.type=so101_leader \
-    --teleop.port=/dev/ttyACM0 \
-    --teleop.id=leader
-```
-
-And then launch the teleop script.
-
-<details>
-<summary>Click to expand code example</summary>
-
-```python
-# envhub_teleop_example.py
-
-import logging
-import time
-import gymnasium as gym
-
-from dataclasses import asdict, dataclass
-from pprint import pformat
-
-from lerobot.teleoperators import (  # noqa: F401
-    Teleoperator,
-    TeleoperatorConfig,
-    make_teleoperator_from_config,
-    so101_leader,
-)
-from lerobot.utils.robot_utils import precise_sleep
-from lerobot.utils.utils import init_logging
-from lerobot.envs.factory import make_env
-
-
-@dataclass
-class TeleoperateConfig:
-    teleop: TeleoperatorConfig
-    env_name: str = "so101_pick_orange"
-    fps: int = 60
-
-
-@dataclass
-class EnvWrap:
-    env: gym.Env
-
-
-def make_env_from_leisaac(env_name: str = "so101_pick_orange"):
-    envs_dict = make_env(
-        f'LightwheelAI/leisaac_env:envs/{env_name}.py',
-        n_envs=1,
-        trust_remote_code=True
-    )
-    suite_name = next(iter(envs_dict))
-    sync_vector_env = envs_dict[suite_name][0]
-    env = sync_vector_env.envs[0].unwrapped
-
-    return env
-
-
-def teleop_loop(teleop: Teleoperator, env: gym.Env, fps: int):
-    from leisaac.devices.action_process import preprocess_device_action
-    from leisaac.assets.robots.lerobot import SO101_FOLLOWER_MOTOR_LIMITS
-    from leisaac.utils.env_utils import dynamic_reset_gripper_effort_limit_sim
-
-    env_wrap = EnvWrap(env=env)
-
-    obs, info = env.reset()
-    while True:
-        loop_start = time.perf_counter()
-        if env.cfg.dynamic_reset_gripper_effort_limit:
-            dynamic_reset_gripper_effort_limit_sim(env, 'so101leader')
-
-        raw_action = teleop.get_action()
-        processed_action = preprocess_device_action(
-            dict(
-                so101_leader=True,
-                joint_state={
-                    k.removesuffix(".pos"): v for k, v in raw_action.items()},
-                motor_limits=SO101_FOLLOWER_MOTOR_LIMITS),
-            env_wrap
-        )
-        obs, reward, terminated, truncated, info = env.step(processed_action)
-        if terminated or truncated:
-            obs, info = env.reset()
-
-        dt_s = time.perf_counter() - loop_start
-        precise_sleep(1 / fps - dt_s)
-        loop_s = time.perf_counter() - loop_start
-        print(f"\ntime: {loop_s * 1e3:.2f}ms ({1 / loop_s:.0f} Hz)")
-
-
-def teleoperate(cfg: TeleoperateConfig):
-    init_logging()
-    logging.info(pformat(asdict(cfg)))
-
-    teleop = make_teleoperator_from_config(cfg.teleop)
-    env = make_env_from_leisaac(cfg.env_name)
-
-    teleop.connect()
-    if hasattr(env, 'initialize'):
-        env.initialize()
-    try:
-        teleop_loop(teleop=teleop, env=env, fps=cfg.fps)
-    except KeyboardInterrupt:
-        pass
-    finally:
-        teleop.disconnect()
-        env.close()
-
-
-def main():
-    teleoperate(TeleoperateConfig(
-        teleop=so101_leader.SO101LeaderConfig(
-            port="/dev/ttyACM0",
-            id='leader',
-            use_degrees=False,
-        ),
-        env_name="so101_pick_orange",
-        fps=60,
-    ))
-
-
-if __name__ == "__main__":
-    main()
-
-```
-
-</details>
-
-```bash
-python envhub_teleop_example.py
-```
-
-Running the script lets you operate the simulated arm using the physical Leader device.
-
-## ☁️ Cloud Simulation (No GPU Required)
-
-Don’t have a local GPU or the right drivers? No problem! You can run LeIsaac entirely in the cloud with zero setup.
-LeIsaac works out-of-the-box on **NVIDIA Brev**, giving you a fully configured environment directly in your browser.
-
-👉 **Start here:** [https://lightwheelai.github.io/leisaac/docs/cloud_simulation/nvidia_brev](https://lightwheelai.github.io/leisaac/docs/cloud_simulation/nvidia_brev)
-
-Once your instance is deployed, simply open the link for **port 80 (HTTP)** to launch **Visual Studio Code Server** (default password: `password`). From there, you can run simulations, edit code, and visualize IsaacLab environments — all from your web browser.
-
-**No GPU, no drivers, no local installation. Just click and run.**
-
-## Additional Notes
-
-We keep EnvHub coverage aligned with the LeIsaac task. Currently supported:
-
-- `so101_pick_orange`
-- `so101_lift_cube`
-- `so101_clean_toytable`
-- `bi_so101_fold_cloth`
-
-Switch tasks by targeting a different script when calling `make_env`, for example:
-
-```python
-envs_dict_pick_orange = make_env("LightwheelAI/leisaac_env:envs/so101_pick_orange.py", n_envs=1, trust_remote_code=True)
-envs_dict_lift_cube = make_env("LightwheelAI/leisaac_env:envs/so101_lift_cube.py", n_envs=1, trust_remote_code=True)
-envs_dict_clean_toytable = make_env("LightwheelAI/leisaac_env:envs/so101_clean_toytable.py", n_envs=1, trust_remote_code=True)
-envs_dict_fold_cloth = make_env("LightwheelAI/leisaac_env:envs/bi_so101_fold_cloth.py", n_envs=1, trust_remote_code=True)
-```
-
-Note: when working with `bi_so101_fold_cloth`, call `initialize()` immediately after retrieving the env before performing any other operations:
-
-<details>
-<summary>Click to expand code example</summary>
-
-```python
-import torch
-from lerobot.envs.factory import make_env
-
-# Load from the hub
-envs_dict = make_env("LightwheelAI/leisaac_env:envs/bi_so101_fold_cloth.py", n_envs=1, trust_remote_code=True)
-
-# Access the environment
-suite_name = next(iter(envs_dict))
-sync_vector_env = envs_dict[suite_name][0]
-# retrieve the isaac environment from the sync vector env
-env = sync_vector_env.envs[0].unwrapped
-
-# NOTE: initialize() first
-env.initialize()
-
-# other operation with env...
-```
-
-</details>

docs/source/il_robots.mdx

@@ -393,7 +393,7 @@ import time
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
 from lerobot.robots.so100_follower.so100_follower import SO100Follower
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.utils import log_say
 
 episode_idx = 0
@@ -415,7 +415,7 @@ for idx in range(dataset.num_frames):
     }
     robot.send_action(action)
 
-    precise_sleep(1.0 / dataset.fps - (time.perf_counter() - t0))
+    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
 
 robot.disconnect()
 ```
@@ -428,7 +428,7 @@ Your robot should replicate movements similar to those you recorded. For example
 
 ## Train a policy
 
-To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
 
 ```bash
 lerobot-train \
@@ -485,7 +485,7 @@ huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
 
 ## Run inference and evaluate your policy
 
-You can use the `record` script from [`lerobot-record`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
+You can use the `record` script from [`lerobot/record.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/record.py) with a policy checkpoint as input, to run inference and evaluate your policy. For instance, run this command or API example to run inference and record 10 evaluation episodes:
 
 <hfoptions id="eval">
 <hfoption id="Command">

docs/source/il_sim.mdx

@@ -0,0 +1,220 @@
+# Imitation Learning in Sim
+
+This tutorial will explain how to train a neural network to control a robot in simulation with imitation learning.
+
+**You'll learn:**
+
+1. How to record a dataset in simulation with [gym-hil](https://github.com/huggingface/gym-hil) and visualize the dataset.
+2. How to train a policy using your data.
+3. How to evaluate your policy in simulation and visualize the results.
+
+For the simulation environment we use the same [repo](https://github.com/huggingface/gym-hil) that is also being used by the Human-In-the-Loop (HIL) reinforcement learning algorithm.
+This environment is based on [MuJoCo](https://mujoco.org) and allows you to record datasets in LeRobotDataset format.
+Teleoperation is easiest with a controller like the Logitech F710, but you can also use your keyboard if you are up for the challenge.
+
+## Installation
+
+First, install the `gym_hil` package within the LeRobot environment, go to your LeRobot folder and run this command:
+
+```bash
+pip install -e ".[hilserl]"
+```
+
+## Teleoperate and Record a Dataset
+
+To use `gym_hil` with LeRobot, you need to use a configuration file. An example config file can be found [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/sim_il/env_config.json).
+
+To teleoperate and collect a dataset, we need to modify this config file. Here's an example configuration for imitation learning data collection:
+
+```json
+{
+  "env": {
+    "type": "gym_manipulator",
+    "name": "gym_hil",
+    "task": "PandaPickCubeGamepad-v0",
+    "fps": 10
+  },
+  "dataset": {
+    "repo_id": "your_username/il_gym",
+    "root": null,
+    "task": "pick_cube",
+    "num_episodes_to_record": 30,
+    "replay_episode": null,
+    "push_to_hub": true
+  },
+  "mode": "record",
+  "device": "cuda"
+}
+```
+
+Key configuration points:
+
+- Set your `repo_id` in the `dataset` section: `"repo_id": "your_username/il_gym"`
+- Set `num_episodes_to_record: 30` to collect 30 demonstration episodes
+- Ensure `mode` is set to `"record"`
+- If you don't have an NVIDIA GPU, change `"device": "cuda"` to `"mps"` for macOS or `"cpu"`
+- To use keyboard instead of gamepad, change `"task"` to `"PandaPickCubeKeyboard-v0"`
+
+Then we can run this command to start:
+
+<hfoptions id="teleop_sim">
+<hfoption id="Linux">
+
+```bash
+python -m lerobot.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
+```
+
+</hfoption>
+<hfoption id="MacOS">
+
+```bash
+mjpython -m lerobot.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json
+```
+
+</hfoption>
+</hfoptions>
+
+Once rendered you can teleoperate the robot with the gamepad or keyboard, below you can find the gamepad/keyboard controls.
+
+Note that to teleoperate the robot you have to hold the "Human Take Over Pause Policy" Button `RB` to enable control!
+
+**Gamepad Controls**
+
+<p align="center">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/gamepad_guide.jpg?raw=true"
+    alt="Figure shows the control mappings on a Logitech gamepad."
+    title="Gamepad Control Mapping"
+    width="100%"
+  ></img>
+</p>
+<p align="center">
+  <i>Gamepad button mapping for robot control and episode management</i>
+</p>
+
+**Keyboard controls**
+
+For keyboard controls use the `spacebar` to enable control and the following keys to move the robot:
+
+```bash
+  Arrow keys: Move in X-Y plane
+  Shift and Shift_R: Move in Z axis
+  Right Ctrl and Left Ctrl: Open and close gripper
+  ESC: Exit
+```
+
+## Visualize a dataset
+
+If you uploaded your dataset to the hub you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id.
+
+<p align="center">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/dataset_visualizer_sim.png"
+    alt="Figure shows the dataset visualizer"
+    title="Dataset visualization"
+    width="100%"
+  ></img>
+</p>
+<p align="center">
+  <i>Dataset visualizer</i>
+</p>
+
+## Train a policy
+
+To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+
+```bash
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/il_gym \
+  --policy.type=act \
+  --output_dir=outputs/train/il_sim_test \
+  --job_name=il_sim_test \
+  --policy.device=cuda \
+  --wandb.enable=true
+```
+
+Let's explain the command:
+
+1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+3. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
+4. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
+
+Training should take several hours, 100k steps (which is the default) will take about 1h on Nvidia A100. You will find checkpoints in `outputs/train/il_sim_test/checkpoints`.
+
+#### Train using Collab
+
+If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act).
+
+#### Upload policy checkpoints
+
+Once training is done, upload the latest checkpoint with:
+
+```bash
+huggingface-cli upload ${HF_USER}/il_sim_test \
+  outputs/train/il_sim_test/checkpoints/last/pretrained_model
+```
+
+You can also upload intermediate checkpoints with:
+
+```bash
+CKPT=010000
+huggingface-cli upload ${HF_USER}/il_sim_test${CKPT} \
+  outputs/train/il_sim_test/checkpoints/${CKPT}/pretrained_model
+```
+
+## Evaluate your policy in Sim
+
+To evaluate your policy we have to use a configuration file. An example can be found [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/sim_il/eval_config.json).
+
+Here's an example evaluation configuration:
+
+```json
+{
+  "env": {
+    "type": "gym_manipulator",
+    "name": "gym_hil",
+    "task": "PandaPickCubeGamepad-v0",
+    "fps": 10
+  },
+  "dataset": {
+    "repo_id": "your_username/il_sim_dataset",
+    "dataset_root": null,
+    "task": "pick_cube"
+  },
+  "pretrained_policy_name_or_path": "your_username/il_sim_model",
+  "device": "cuda"
+}
+```
+
+Make sure to replace:
+
+- `repo_id` with the dataset you trained on (e.g., `your_username/il_sim_dataset`)
+- `pretrained_policy_name_or_path` with your model ID (e.g., `your_username/il_sim_model`)
+
+Then you can run this command to visualize your trained policy
+
+<hfoptions id="eval_policy">
+<hfoption id="Linux">
+
+```bash
+python -m lerobot.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
+```
+
+</hfoption>
+<hfoption id="MacOS">
+
+```bash
+mjpython -m lerobot.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json
+```
+
+</hfoption>
+</hfoptions>
+
+> [!WARNING]
+> While the main workflow of training ACT in simulation is straightforward, there is significant room for exploring how to set up the task, define the initial state of the environment, and determine the type of data required during collection to learn the most effective policy. If your trained policy doesn't perform well, investigate the quality of the dataset it was trained on using our visualizers, as well as the action values and various hyperparameters related to ACT and the simulation.
+
+Congrats 🎉, you have finished this tutorial. If you want to continue with using LeRobot in simulation follow this [Tutorial on reinforcement learning in sim with HIL-SERL](https://huggingface.co/docs/lerobot/hilserl_sim)
+
+> [!TIP]
+> If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb).

docs/source/installation.mdx

@@ -90,7 +90,7 @@ If you encounter build errors, you may need to install additional dependencies:
 To install these for linux run:
 
 ```bash
-sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev
+sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config
 ```
 
 For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)

docs/source/libero.mdx

@@ -62,11 +62,6 @@ lerobot-eval \
 
 - Pass a comma-separated list to `--env.task` for multi-suite evaluation.
 
-### Control Mode
-
-LIBERO now supports two control modes: relative and absolute. This matters because different VLA checkpoints are trained with different mode of action to output hence control parameterizations.
-You can switch them with: `env.control_mode = "relative"` and `env.control_mode = "absolute"`
-
 ### Policy inputs and outputs
 
 When using LIBERO through LeRobot, policies interact with the environment via **observations** and **actions**:

docs/source/so101.mdx

@@ -30,6 +30,131 @@ The follower arm uses 6x STS3215 motors with 1/345 gearing. The leader, however,
 | Wrist Roll          |   5   |  1 / 147   |
 | Gripper             |   6   |  1 / 147   |
 
+### Clean Parts
+
+Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material.
+
+It is advisable to install one 3-pin cable in the motor after placing them before continuing assembly.
+
+### Joint 1
+
+- Place the first motor into the base.
+- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from the bottom.
+- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
+- Install both motor horns, securing the top horn with a M3x6mm screw.
+- Attach the shoulder part.
+- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
+- Add the shoulder motor holder.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint1_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Joint 2
+
+- Slide the second motor in from the top.
+- Fasten the second motor with 4 M2x6mm screws.
+- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
+- Attach the upper arm with 4 M3x6mm screws on each side.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint2_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Joint 3
+
+- Insert motor 3 and fasten using 4 M2x6mm screws
+- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
+- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint3_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Joint 4
+
+- Slide over motor holder 4.
+- Slide in motor 4.
+- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint4_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Joint 5
+
+- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
+- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
+- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint5_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+### Gripper / Handle
+
+<hfoptions id="assembly">
+<hfoption id="Follower">
+
+- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
+- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
+- Attach the motor horns and again use a M3x6mm horn screw.
+- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Gripper_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+</hfoption>
+<hfoption id="Leader">
+
+- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
+- Attach the handle to motor 5 using 1 M2x6mm screw.
+- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
+- Attach the follower trigger with 4 M3x6mm screws.
+
+<div class="video-container">
+  <video controls width="600">
+    <source
+      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Leader_v2.mp4"
+      type="video/mp4"
+    />
+  </video>
+</div>
+
+</hfoption>
+</hfoptions>
+
 ## Configure the motors
 
 ### 1. Find the USB ports associated with each arm
@@ -215,131 +340,6 @@ leader.setup_motors()
 </hfoption>
 </hfoptions>
 
-### Clean Parts
-
-Remove all support material from the 3D-printed parts. The easiest way to do this is using a small screwdriver to get underneath the support material.
-
-It is advisable to install one 3-pin cable in the motor after placing them before continuing assembly.
-
-### Joint 1
-
-- Place the first motor into the base.
-- Fasten the motor with 4 M2x6mm screws (smallest screws). Two from the top and two from the bottom.
-- Slide over the first motor holder and fasten it using two M2x6mm screws (one on each side).
-- Install both motor horns, securing the top horn with a M3x6mm screw.
-- Attach the shoulder part.
-- Tighten the shoulder part with 4 M3x6mm screws on top and 4 M3x6mm screws on the bottom
-- Add the shoulder motor holder.
-
-<div class="video-container">
-  <video controls width="600">
-    <source
-      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint1_v2.mp4"
-      type="video/mp4"
-    />
-  </video>
-</div>
-
-### Joint 2
-
-- Slide the second motor in from the top.
-- Fasten the second motor with 4 M2x6mm screws.
-- Attach both motor horns to motor 2, again use the M3x6mm horn screw.
-- Attach the upper arm with 4 M3x6mm screws on each side.
-
-<div class="video-container">
-  <video controls width="600">
-    <source
-      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint2_v2.mp4"
-      type="video/mp4"
-    />
-  </video>
-</div>
-
-### Joint 3
-
-- Insert motor 3 and fasten using 4 M2x6mm screws
-- Attach both motor horns to motor 3 and secure one again with a M3x6mm horn screw.
-- Connect the forearm to motor 3 using 4 M3x6mm screws on each side.
-
-<div class="video-container">
-  <video controls width="600">
-    <source
-      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint3_v2.mp4"
-      type="video/mp4"
-    />
-  </video>
-</div>
-
-### Joint 4
-
-- Slide over motor holder 4.
-- Slide in motor 4.
-- Fasten motor 4 with 4 M2x6mm screws and attach its motor horns, use a M3x6mm horn screw.
-
-<div class="video-container">
-  <video controls width="600">
-    <source
-      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint4_v2.mp4"
-      type="video/mp4"
-    />
-  </video>
-</div>
-
-### Joint 5
-
-- Insert motor 5 into the wrist holder and secure it with 2 M2x6mm front screws.
-- Install only one motor horn on the wrist motor and secure it with a M3x6mm horn screw.
-- Secure the wrist to motor 4 using 4 M3x6mm screws on both sides.
-
-<div class="video-container">
-  <video controls width="600">
-    <source
-      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Joint5_v2.mp4"
-      type="video/mp4"
-    />
-  </video>
-</div>
-
-### Gripper / Handle
-
-<hfoptions id="assembly">
-<hfoption id="Follower">
-
-- Attach the gripper to motor 5, attach it to the motor horn on the wrist using 4 M3x6mm screws.
-- Insert the gripper motor and secure it with 2 M2x6mm screws on each side.
-- Attach the motor horns and again use a M3x6mm horn screw.
-- Install the gripper claw and secure it with 4 M3x6mm screws on both sides.
-
-<div class="video-container">
-  <video controls width="600">
-    <source
-      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Gripper_v2.mp4"
-      type="video/mp4"
-    />
-  </video>
-</div>
-
-</hfoption>
-<hfoption id="Leader">
-
-- Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
-- Attach the handle to motor 5 using 1 M2x6mm screw.
-- Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
-- Attach the follower trigger with 4 M3x6mm screws.
-
-<div class="video-container">
-  <video controls width="600">
-    <source
-      src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/Leader_v2.mp4"
-      type="video/mp4"
-    />
-  </video>
-</div>
-
-</hfoption>
-</hfoptions>
-
 ## Calibrate
 
 Next, you'll need to calibrate your robot to ensure that the leader and follower arms have the same position values when they are in the same physical position.

docs/source/torch_accelerators.mdx

@@ -1,42 +0,0 @@
-# PyTorch accelerators
-
-LeRobot supports multiple hardware acceleration options for both training and inference.
-
-These options include:
-
-- **CPU**: CPU executes all computations, no dedicated accelerator is used
-- **CUDA**: acceleration with NVIDIA & AMD GPUs
-- **MPS**: acceleration with Apple Silicon GPUs
-- **XPU**: acceleration with Intel integrated and discrete GPUs
-
-## Getting Started
-
-To use particular accelerator, a suitable version of PyTorch should be installed.
-
-For CPU, CUDA, and MPS backends follow instructions provided on [PyTorch installation page](https://pytorch.org/get-started/locally).
-For XPU backend, follow instructions from [PyTorch documentation](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html).
-
-### Verifying the installation
-
-After installation, accelerator availability can be verified by running
-
-```python
-import torch
-print(torch.<backend_name>.is_available())  # <backend_name> is cuda, mps, or xpu
-```
-
-## How to run training or evaluation
-
-To select the desired accelerator, use the `--policy.device` flag when running `lerobot-train` or `lerobot-eval`. For example, to use MPS on Apple Silicon, run:
-
-```bash
-lerobot-train
-    --policy.device=mps ...
-```
-
-```bash
-lerobot-eval \
-    --policy.device=mps ...
-```
-
-However, in most cases, presence of an accelerator is detected automatically and `policy.device` parameter can be omitted from CLI commands.

docs/source/unitree_g1.mdx

@@ -1,203 +0,0 @@
-# Unitree G1 Robot Setup and Control
-
-This guide covers the complete setup process for the Unitree G1 humanoid, from initial connection to running gr00t_wbc locomotion.
-
-## About the Unitree G1
-
-We offer support for both 29 and 23 DOF G1. In this first PR we introduce:
-
-- **`unitree g1` robot class, handling low level communication with the humanoid**
-- **ZMQ socket bridge** for remote communication over WiFi, allowing one to deploy policies remotely instead of over ethernet or directly on the Orin
-- **GR00T locomotion policy** for bipedal walking and balance
-
----
-
-## Part 1: Connect to Robot over Ethernet
-
-### Step 1: Configure Your Computer's Ethernet Interface
-
-Set a static IP on the same subnet as the robot:
-
-```bash
-# Replace 'enp131s0' with your ethernet interface name (check with `ip a`)
-sudo ip addr flush dev enp131s0
-sudo ip addr add 192.168.123.200/24 dev enp131s0
-sudo ip link set enp131s0 up
-```
-
-**Note**: The robot's Ethernet IP is fixed at `192.168.123.164`. Your computer must use `192.168.123.x` where x ≠ 164.
-
-### Step 2: SSH into the Robot
-
-```bash
-ssh unitree@192.168.123.164
-# Password: 123
-```
-
-You should now be connected to the robot's onboard computer.
-
----
-
-## Part 2: Enable WiFi on the Robot
-
-Once connected via Ethernet, follow these steps to enable WiFi:
-
-### Step 1: Enable WiFi Hardware
-
-```bash
-# Unblock WiFi radio
-sudo rfkill unblock wifi
-sudo rfkill unblock all
-
-# Bring up WiFi interface
-sudo ip link set wlan0 up
-
-# Enable NetworkManager control
-sudo nmcli radio wifi on
-sudo nmcli device set wlan0 managed yes
-sudo systemctl restart NetworkManager
-```
-
-### Step 2: Enable Internet Forwarding
-
-**On your laptop:**
-
-```bash
-# Enable IP forwarding
-sudo sysctl -w net.ipv4.ip_forward=1
-
-# Set up NAT (replace wlp132s0f0 with your WiFi interface)
-sudo iptables -t nat -A POSTROUTING -o wlp132s0f0 -s 192.168.123.0/24 -j MASQUERADE
-sudo iptables -A FORWARD -i wlp132s0f0 -o enp131s0 -m state --state RELATED,ESTABLISHED -j ACCEPT
-sudo iptables -A FORWARD -i enp131s0 -o wlp132s0f0 -j ACCEPT
-```
-
-**On the robot:**
-
-```bash
-# Add laptop as default gateway
-sudo ip route del default 2>/dev/null || true
-sudo ip route add default via 192.168.123.200 dev eth0
-echo "nameserver 8.8.8.8" | sudo tee /etc/resolv.conf
-
-# Test connection
-ping -c 3 8.8.8.8
-```
-
-### Step 3: Connect to WiFi Network
-
-```bash
-# List available networks
-nmcli device wifi list
-
-# Connect to your WiFi (example)
-sudo nmcli connection add type wifi ifname wlan0 con-name "YourNetwork" ssid "YourNetwork"
-sudo nmcli connection modify "YourNetwork" wifi-sec.key-mgmt wpa-psk
-sudo nmcli connection modify "YourNetwork" wifi-sec.psk "YourPassword"
-sudo nmcli connection modify "YourNetwork" connection.autoconnect yes
-sudo nmcli connection up "YourNetwork"
-
-# Check WiFi IP address
-ip a show wlan0
-```
-
-### Step 4: SSH Over WiFi
-
-Once connected to WiFi, note the robot's IP address and disconnect the Ethernet cable. You can now SSH over WiFi:
-
-```bash
-ssh unitree@<YOUR_ROBOT_IP>
-# Password: 123
-```
-
-Replace `<YOUR_ROBOT_IP>` with your robot's actual WiFi IP address (e.g., `172.18.129.215`).
-
----
-
-## Part 3: Robot Server Setup
-
-### Step 1: Install LeRobot on the Orin
-
-SSH into the robot and install LeRobot:
-
-```bash
-ssh unitree@<YOUR_ROBOT_IP>
-
-conda create -y -n lerobot python=3.10
-conda activate lerobot
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e '.[unitree_g1]'
-git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
-cd unitree_sdk2_python  && pip install -e .
-```
-
-**Note**: The Unitree SDK requires CycloneDDS v0.10.2 to be installed. See the [Unitree SDK documentation](https://github.com/unitreerobotics/unitree_sdk2_python) for details.
-
-### Step 2: Run the Robot Server
-
-On the robot:
-
-```bash
-python src/lerobot/robots/unitree_g1/run_g1_server.py
-```
-
-**Important**: Keep this terminal running. The server must be active for remote control.
-
----
-
-## Part 4: Running GR00T Locomotion
-
-With the robot server running, you can now control the robot from your laptop.
-
-### Step 1: Install LeRobot on your machine
-
-```bash
-conda create -y -n lerobot python=3.10
-conda activate lerobot
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e '.[unitree_g1]'
-git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
-cd unitree_sdk2_python  && pip install -e .
-```
-
-### Step 2: Update Robot IP in Config
-
-Edit the config file to match your robot's WiFi IP:
-
-```python
-# In src/lerobot/robots/unitree_g1/config_unitree_g1.py
-robot_ip: str = "<YOUR_ROBOT_IP>"  # Replace with your robot's WiFi IP.
-```
-
-**Note**: When running directly on the G1 (not remotely), set `robot_ip: str = "127.0.0.1"` instead.
-
-### Step 3: Run the Locomotion Policy
-
-```bash
-# Run GR00T locomotion controller
-python examples/unitree_g1/gr00t_locomotion.py --repo-id "nepyope/GR00T-WholeBodyControl_g1"
-```
-
-### Step 4: Control with Remote
-
-- **Left stick**: Forward/backward and left/right movement
-- **Right stick**: Rotation
-- **R1 button**: Raise waist height
-- **R2 button**: Lower waist height
-
-Press `Ctrl+C` to stop the policy.
-
----
-
-## Additional Resources
-
-- [Unitree SDK Documentation](https://github.com/unitreerobotics/unitree_sdk2_python)
-- [GR00T Policy Repository](https://huggingface.co/nepyope/GR00T-WholeBodyControl_g1)
-- [LeRobot Documentation](https://github.com/huggingface/lerobot)
-- [Unitree_IL_Lerobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
-
----
-
-_Last updated: December 2025_

docs/source/using_dataset_tools.mdx

@@ -11,14 +11,13 @@ LeRobot provides several utilities for manipulating datasets:
 3. **Merge Datasets** - Combine multiple datasets into one. The datasets must have identical features, and episodes are concatenated in the order specified in `repo_ids`
 4. **Add Features** - Add new features to a dataset
 5. **Remove Features** - Remove features from a dataset
-6. **Convert to Video** - Convert image-based datasets to video format for efficient storage
 
 The core implementation is in `lerobot.datasets.dataset_tools`.
 An example script detailing how to use the tools API is available in `examples/dataset/use_dataset_tools.py`.
 
 ## Command-Line Tool: lerobot-edit-dataset
 
-`lerobot-edit-dataset` is a command-line script for editing datasets. It can be used to delete episodes, split datasets, merge datasets, add features, remove features, and convert image datasets to video format.
+`lerobot-edit-dataset` is a command-line script for editing datasets. It can be used to delete episodes, split datasets, merge datasets, add features, and remove features.
 
 Run `lerobot-edit-dataset --help` for more information on the configuration of each operation.
 
@@ -87,71 +86,9 @@ lerobot-edit-dataset \
     --operation.feature_names "['observation.images.top']"
 ```
 
-#### Convert to Video
-
-Convert an image-based dataset to video format, creating a new LeRobotDataset where images are stored as videos. This is useful for reducing storage requirements and improving data loading performance. The new dataset will have the exact same structure as the original, but with images encoded as MP4 videos in the proper LeRobot format.
-
-```bash
-# Local-only: Save to a custom output directory (no hub push)
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --operation.type convert_to_video \
-    --operation.output_dir /path/to/output/pusht_video
-
-# Save with new repo_id (local storage)
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --new_repo_id lerobot/pusht_video \
-    --operation.type convert_to_video
-
-# Convert and push to Hugging Face Hub
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --new_repo_id lerobot/pusht_video \
-    --operation.type convert_to_video \
-    --push_to_hub true
-
-# Convert with custom video codec and quality settings
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --operation.type convert_to_video \
-    --operation.output_dir outputs/pusht_video \
-    --operation.vcodec libsvtav1 \
-    --operation.pix_fmt yuv420p \
-    --operation.g 2 \
-    --operation.crf 30
-
-# Convert only specific episodes
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --operation.type convert_to_video \
-    --operation.output_dir outputs/pusht_video \
-    --operation.episode_indices "[0, 1, 2, 5, 10]"
-
-# Convert with multiple workers for parallel processing
-lerobot-edit-dataset \
-    --repo_id lerobot/pusht_image \
-    --operation.type convert_to_video \
-    --operation.output_dir outputs/pusht_video \
-    --operation.num_workers 8
-```
-
-**Parameters:**
-
-- `output_dir`: Custom output directory (optional - by default uses `new_repo_id` or `{repo_id}_video`)
-- `vcodec`: Video codec to use - options: `h264`, `hevc`, `libsvtav1` (default: `libsvtav1`)
-- `pix_fmt`: Pixel format - options: `yuv420p`, `yuv444p` (default: `yuv420p`)
-- `g`: Group of pictures (GOP) size - lower values give better quality but larger files (default: 2)
-- `crf`: Constant rate factor - lower values give better quality but larger files, 0 is lossless (default: 30)
-- `fast_decode`: Fast decode tuning option (default: 0)
-- `episode_indices`: List of specific episodes to convert (default: all episodes)
-- `num_workers`: Number of parallel workers for processing (default: 4)
-
-**Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). All episodes, stats, and tasks are preserved.
-
 ### Push to Hub
 
-Add the `--push_to_hub true` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:
+Add the `--push_to_hub` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:
 
 ```bash
 lerobot-edit-dataset \
@@ -159,7 +96,7 @@ lerobot-edit-dataset \
     --new_repo_id lerobot/pusht_after_deletion \
     --operation.type delete_episodes \
     --operation.episode_indices "[0, 2, 5]" \
-    --push_to_hub true
+    --push_to_hub
 ```
 
 There is also a tool for adding features to a dataset that is not yet covered in `lerobot-edit-dataset`.

docs/source/xvla.mdx

@@ -1,528 +0,0 @@
-# X-VLA: The First Soft-Prompted Robot Foundation Model for Any Robot, Any Task
-
-## Overview
-
-For years, robotics has aspired to build agents that can follow natural human instructions and operate dexterously across many environments and robot bodies. Recent breakthroughs in LLMs and VLMs suggest a path forward: extend these foundation-model architectures to embodied control by grounding them in actions. This has led to the rise of Vision-Language-Action (VLA) models, with the hope that a single generalist model could combine broad semantic understanding with robust manipulation skills.
-
-But training such models is difficult. Robot data is fragmented across platforms, sensors, embodiments, and collection protocols. Heterogeneity appears everywhere: different arm configurations, different action spaces, different camera setups, different visual domains, and different task distributions. These inconsistencies create major distribution shifts that make pretraining unstable and adaptation unreliable.
-
-Inspired by meta-learning and prompt learning, we ask: **"What if a VLA model could learn the structure of each robot and dataset the same way LLMs learn tasks, through prompts?"**
-
-**X-VLA** is a soft-prompted, flow-matching VLA framework that treats each hardware setup as a "task" and encodes it using a small set of learnable embeddings. These **Soft Prompts** capture embodiment and domain-specific variations, guiding the Transformer from the earliest stages of multimodal fusion. With this mechanism, X-VLA can reconcile diverse robot morphologies, data types, and sensor setups within a single unified architecture.
-
-<p align="center">
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-architecture.png"
-    alt="XVLA Architecture"
-    style="max-width: 100%; height: auto; width: 800px;"
-  />
-</p>
-
-Built from pure Transformer encoders, X-VLA scales naturally with model size and dataset diversity. Across 6 simulation benchmarks and 3 real robots, Soft Prompts consistently outperform existing methods in handling hardware and domain differences. X-VLA-0.9B, trained on 290K episodes spanning seven robotic platforms, learns an embodiment-agnostic generalist policy in Phase I, and adapts efficiently to new robots in Phase II simply by learning a new set of prompts, while keeping the backbone frozen.
-
-<p align="center">
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-architecture2.png"
-    alt="XVLA Architecture 2"
-    style="width: 60%; height: auto;"
-  />
-</p>
-
-With only 1% of parameters tuned (9M), X-VLA-0.9B achieves near-π₀ performance on LIBERO and Simpler-WidowX, despite using **300× fewer trainable parameters**. It also demonstrates strong real-world dexterity with minimal demonstrations, including folding cloths in under two minutes.
-
-<p align="center">
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-fold.png"
-    alt="XVLA fold visualization"
-    style="width: 95%; max-width: 1100px; height: auto;"
-  />
-</p>
-
-X-VLA shows that generalist robot intelligence does not require increasingly complex architectures, only the right way to absorb heterogeneity. Soft Prompts offer a simple, scalable mechanism for unifying diverse robotic data, paving the way toward adaptable, cross-embodiment robot foundation models.
-
-## Installation
-
-After installing LeRobot, install the X-VLA dependencies:
-
-```bash
-pip install -e .[xvla]
-```
-
-After the new release, you'll be able to do:
-
-```bash
-pip install lerobot[xvla]
-```
-
-## Quick Start
-
-### Basic Usage
-
-To use X-VLA in your LeRobot configuration, specify the policy type as:
-
-```bash
-policy.type=xvla
-```
-
-### Evaluating Pre-trained Checkpoints
-
-Example evaluation with LIBERO:
-
-```bash
-lerobot-eval \
-  --policy.path="lerobot/xvla-libero" \
-  --env.type=libero \
-  --env.task=libero_spatial,libero_goal,libero_10 \
-  --env.control_mode=absolute \
-  --eval.batch_size=1 \
-  --eval.n_episodes=1 \
-  --env.episode_length=800 \
-  --seed=142
-```
-
-## Available Checkpoints
-
-### 🎯 Base Model
-
-**[lerobot/xvla-base](https://huggingface.co/lerobot/xvla-base)**
-
-A 0.9B parameter instantiation of X-VLA, trained with a carefully designed data processing and learning recipe. The training pipeline consists of two phases:
-
-- **Phase I: Pretraining** - Pretrained on 290K episodes from Droid, Robomind, and Agibot, spanning seven platforms across five types of robotic arms (single-arm to bi-manual setups). By leveraging soft prompts to absorb embodiment-specific variations, the model learns an embodiment-agnostic generalist policy.
-
-- **Phase II: Domain Adaptation** - Adapted to deployable policies for target domains. A new set of soft prompts is introduced and optimized to encode the hardware configuration of the novel domain, while the pretrained backbone remains frozen.
-
-### Simulation Checkpoints
-
-**[lerobot/xvla-libero](https://huggingface.co/lerobot/xvla-libero)**
-
-Achieves 93% success rate on LIBERO benchmarks. Fine-tuned from the base model for simulation tasks.
-
-**[lerobot/xvla-widowx](https://huggingface.co/lerobot/xvla-widowx)**
-
-Fine-tuned on BridgeData for pick-and-place experiments on compact WidowX platforms. Demonstrates robust manipulation capabilities.
-
-### 🤖 Real-World Checkpoints
-
-**[lerobot/xvla-folding](https://huggingface.co/lerobot/xvla-folding)**
-
-A fine-tuned dexterous manipulation model trained on the high-quality Soft-FOLD cloth folding dataset. Achieves 100% success rate over 2 hours of continuous cloth folding.
-
-**[lerobot/xvla-agibot-world](https://huggingface.co/lerobot/xvla-agibot-world)**
-
-Optimized for AgileX robot dexterous manipulation tasks.
-
-**[lerobot/xvla-google-robot](https://huggingface.co/lerobot/xvla-google-robot)**
-
-Adapted for Google Robot platforms.
-
-## Training X-VLA
-
-### Recommended Training Configuration
-
-When fine-tuning X-VLA for a new embodiment or task, we recommend not freezing the VLM, and also setting the `policy.dtype=bfloat16` to not hit OOM errors.
-
-```bash
-lerobot-train \
-  --dataset.repo_id=YOUR_DATASET \
-  --output_dir=./outputs/xvla_training \
-  --job_name=xvla_training \
-  --policy.path="lerobot/xvla-base" \
-  --policy.repo_id="HF_USER/xvla-your-robot" \
-  --policy.dtype=bfloat16 \
-  --steps=3000 \
-  --policy.device=cuda \
-  --policy.freeze_vision_encoder=false \
-  --policy.freeze_language_encoder=false \
-  --policy.train_policy_transformer=true \
-  --policy.train_soft_prompts=true \
-  --policy.action_mode=YOUR_ACTION_MODE
-```
-
-### Training Parameters Explained
-
-| Parameter                  | Default | Description                                    |
-| -------------------------- | ------- | ---------------------------------------------- |
-| `freeze_vision_encoder`    | `false` | Do not freeze the VLM vision encoder weights   |
-| `freeze_language_encoder`  | `false` | Do not freeze the VLM language encoder weights |
-| `train_policy_transformer` | `true`  | Allow policy transformer layers to train       |
-| `train_soft_prompts`       | `true`  | Allow soft prompts to train                    |
-
-**💡 Best Practice**: For Phase II adaptation to new embodiments, do not freeze the VLM encoders and also train the policy transformer and soft prompts.
-
-### Example: Training on Bimanual Robot
-
-```bash
-lerobot-train \
-  --dataset.repo_id=pepijn223/bimanual-so100-handover-cube \
-  --output_dir=./outputs/xvla_bimanual \
-  --job_name=xvla_so101_training \
-  --policy.path="lerobot/xvla-base" \
-  --policy.dtype=bfloat16 \
-  --policy.repo_id="YOUR_USERNAME/xvla-biso101" \
-  --steps=3000 \
-  --policy.device=cuda \
-  --policy.action_mode=so101_bimanual \
-  --policy.freeze_vision_encoder=false \
-  --policy.freeze_language_encoder=false \
-  --policy.train_policy_transformer=true \
-  --policy.train_soft_prompts=true
-```
-
-💡 **Best Performance:** If you have sufficient computational resources and want to achieve best X-VLA finetuning performance, you should follow the official finetuning strategy:
-
-**🔥 Full-finetune all components with a custom learning-rate scheme**
-
-To ensure stable optimization, the Vision-Language Model (VLM) must be trained with only 1/10 of the base learning rate, while all other components use the full LR.
-This LR ratio is crucial for achieving strong and stable finetuning performance. This is already done for you by default.
-❕Note
-
-Completely matching the official reported performance may require an additional warm-up LR schedule for soft-prompts, which can bring minor improvements.
-We encourage implementing this in your customized training pipeline for optimal results.
-
-## Core Concepts
-
-### 1. Action Modes
-
-X-VLA uses an **Action Registry** system to handle different action spaces and embodiments. The `action_mode` parameter defines how actions are processed, what loss functions are used, and how predictions are post-processed.
-
-#### Available Action Modes
-
-| Action Mode      | Action Dim              | Description                                 | Use Case                             |
-| ---------------- | ----------------------- | ------------------------------------------- | ------------------------------------ |
-| `ee6d`           | 20                      | End-effector with xyz, 6D rotation, gripper | Dual-arm setups with spatial control |
-| `joint`          | 14                      | Joint-space with gripper                    | Direct joint control robots          |
-| `agibot_ee6d`    | 20                      | AGI-bot variant with MSE loss               | AGI-bot platforms                    |
-| `so101_bimanual` | 20 (model), 12 (real)   | SO101 bimanual robot                        | Bimanual manipulation tasks          |
-| `auto`           | 20 (model), auto (real) | Auto-detects action dim from dataset        | **Recommended** for new robots       |
-
-#### Why Action Modes Matter
-
-When you have a pretrained checkpoint like `lerobot/xvla-base` trained with `action_dim=20`, and you want to train on a dataset with a different action dimension (e.g., 14 for bimanual arms), you can't simply trim the action dimension. The action mode orchestrates:
-
-1. **Loss Computation**: Different loss functions for different action components (MSE for joints, BCE for grippers, etc.)
-2. **Preprocessing**: Zeroing out gripper channels, padding dimensions
-3. **Postprocessing**: Applying sigmoid to gripper logits, trimming padding
-
-#### Example: BimanualSO101 Action Space
-
-The `so101_bimanual` action mode handles the mismatch between model output (20D) and real robot control (12D):
-
-```python
-# Model outputs 20 dimensions for compatibility
-dim_action = 20
-
-# Real robot only needs 12 dimensions
-# [left_arm (6), right_arm (6)] = [joints (5) + gripper (1)] × 2
-REAL_DIM = 12
-
-# Preprocessing: Pad 12D actions to 20D for training
-# Postprocessing: Trim 20D predictions to 12D for deployment
-```
-
-See the [action_hub.py](/home/jade_choghari/robot/lerobot/src/lerobot/policies/xvla/action_hub.py) implementation for details.
-
-#### Auto Action Mode (Recommended)
-
-The `auto` action mode is the easiest way to use X-VLA with any robot. It automatically detects your dataset's action dimension and handles padding/trimming:
-
-```bash
-lerobot-train \
-  --policy.path="lerobot/xvla-base" \
-  --policy.action_mode=auto \
-  --policy.max_action_dim=20 \
-  ...
-```
-
-**How it works:**
-
-- Reads `action_feature.shape[-1]` from your dataset (e.g., 7 for Franka)
-- Model outputs `max_action_dim` (default 20) for pretrained compatibility
-- Loss is computed **only on the real dimensions**: `MSE(pred[:,:,:real_dim], target[:,:,:real_dim])`
-- Postprocess trims output back to `real_dim` for robot control
-
-This eliminates the need to create custom action modes for most robots.
-
-### 2. Domain IDs
-
-Domain IDs are learnable identifiers for different robot configurations and camera setups. They allow X-VLA to distinguish between:
-
-- Different robots (Robot 1 vs Robot 2)
-- Different camera configurations (cam1 vs cam2)
-- Different combinations (Robot1-cam1-cam2 vs Robot1-cam1 vs Robot2-cam1)
-
-#### Setting Domain IDs
-
-**During Training**: By default, domain_id is set to 0 for general training.
-
-**During Evaluation**: Specify the domain_id that matches your checkpoint's training configuration.
-
-```python
-# Example: LIBERO checkpoint uses domain_id=3
-domain_id = 3
-```
-
-The domain_id is automatically added to observations by the `XVLAAddDomainIdProcessorStep` in the preprocessing pipeline.
-
-The `lerobot/xvla-base` model has been trained on the following domain IDs. It is recommended to choose one that most resembles your robot/configuration:
-
-#### Fine-tuning Datasets
-
-| Dataset Name     | Domain ID |
-| ---------------- | --------- |
-| Bridge           | 0         |
-| RT1              | 1         |
-| Calvin           | 2         |
-| libero           | 3         |
-| widowx-air       | 4         |
-| AIR-AGILEX-HQ    | 5         |
-| robotwin2_abs_ee | 6         |
-| robotwin2_clean  | 6         |
-| robocasa-human   | 7         |
-| VLABench         | 8         |
-| AGIBOT-challenge | 9         |
-| AIR-AGILEX       | 10        |
-| AIRBOT           | 18        |
-
-### 3. Processor Steps
-
-X-VLA requires specific preprocessing and postprocessing steps for proper operation.
-
-#### Required Preprocessing Steps
-
-1. **XVLAImageToFloatProcessorStep**: Converts images from [0, 255] to [0, 1] range
-2. **XVLAImageNetNormalizeProcessorStep**: Applies ImageNet normalization (required for VLM backbone)
-3. **XVLAAddDomainIdProcessorStep**: Adds domain_id to observations
-
-#### Example Custom Processor
-
-For LIBERO environments, a custom processor handles the specific observation format:
-
-```python
-from lerobot.policies.xvla.processor_xvla import LiberoProcessorStep
-
-processor = LiberoProcessorStep()
-# Handles robot_state dictionary, converts rotation matrices to 6D representation
-# Applies 180° image rotation for camera convention
-```
-
-### 4. Configuration Parameters
-
-Key configuration parameters for X-VLA:
-
-```python
-# Observation and action
-n_obs_steps: int = 1          # Number of observation timesteps
-chunk_size: int = 32           # Action sequence length
-n_action_steps: int = 32       # Number of action steps to execute
-
-# Model architecture
-hidden_size: int = 1024        # Transformer hidden dimension
-depth: int = 24                # Number of transformer layers
-num_heads: int = 16            # Number of attention heads
-num_domains: int = 30          # Maximum number of domain IDs
-len_soft_prompts: int = 32     # Length of soft prompt embeddings
-
-# Action space
-action_mode: str = "ee6d"      # Action space type (use "auto" for auto-detection)
-use_proprio: bool = True       # Use proprioceptive state
-max_state_dim: int = 32        # Maximum state dimension
-max_action_dim: int = 20       # Max action dim for padding (used by "auto" mode)
-
-# Vision
-num_image_views: int | None    # Number of camera views
-resize_imgs_with_padding: tuple[int, int] | None  # Target image size with padding
-
-# Training
-num_denoising_steps: int = 10  # Flow matching denoising steps
-```
-
-## Creating Custom Action Modes
-
-If your robot has a unique action space, you can create a custom action mode:
-
-### Step 1: Define Your Action Space
-
-```python
-from lerobot.policies.xvla.action_hub import BaseActionSpace, register_action
-import torch.nn as nn
-
-@register_action("my_custom_robot")
-class MyCustomActionSpace(BaseActionSpace):
-    """Custom action space for my robot."""
-
-    dim_action = 15  # Your robot's action dimension
-    gripper_idx = (7, 14)  # Gripper channel indices
-
-    def __init__(self):
-        super().__init__()
-        self.mse = nn.MSELoss()
-        self.bce = nn.BCEWithLogitsLoss()
-
-    def compute_loss(self, pred, target):
-        """Define your loss computation."""
-        # Example: MSE for joints, BCE for grippers
-        joints_loss = self.mse(pred[:, :, :7], target[:, :, :7])
-        gripper_loss = self.bce(pred[:, :, self.gripper_idx],
-                                target[:, :, self.gripper_idx])
-
-        return {
-            "joints_loss": joints_loss,
-            "gripper_loss": gripper_loss,
-        }
-
-    def preprocess(self, proprio, action, mode="train"):
-        """Preprocess actions before training."""
-        # Example: Zero out grippers in proprioception
-        proprio_m = proprio.clone()
-        action_m = action.clone() if action is not None else None
-        proprio_m[..., self.gripper_idx] = 0.0
-        if action_m is not None:
-            action_m[..., self.gripper_idx] = 0.0
-        return proprio_m, action_m
-
-    def postprocess(self, action):
-        """Post-process predictions for deployment."""
-        # Example: Apply sigmoid to gripper logits
-        action[..., self.gripper_idx] = torch.sigmoid(action[..., self.gripper_idx])
-        return action
-```
-
-### Step 2: Use Your Custom Action Mode
-
-```bash
-lerobot-train \
-  --policy.action_mode=my_custom_robot \
-  --dataset.repo_id=YOUR_DATASET \
-  --policy.path="lerobot/xvla-base" \
-  ...
-```
-
-## Advanced Topics
-
-### Multi-Camera Support
-
-X-VLA supports multiple camera views through the `num_image_views` parameter:
-
-```python
-# Configure for 3 camera views
-policy.num_image_views=3
-
-# Add empty cameras if you have fewer physical cameras
-policy.empty_cameras=1  # Adds 1 zero-padded camera view
-```
-
-### Custom Preprocessing Pipeline
-
-Create a custom preprocessing pipeline for your environment:
-
-```python
-from lerobot.processor import PolicyProcessorPipeline
-from lerobot.policies.xvla.processor_xvla import (
-    XVLAImageToFloatProcessorStep,
-    XVLAImageNetNormalizeProcessorStep,
-    XVLAAddDomainIdProcessorStep,
-)
-
-# Build custom pipeline
-preprocessor = PolicyProcessorPipeline(
-    steps=[
-        YourCustomProcessorStep(),  # Your custom processing
-        XVLAImageToFloatProcessorStep(),  # Required: convert to float
-        XVLAImageNetNormalizeProcessorStep(),  # Required: ImageNet norm
-        XVLAAddDomainIdProcessorStep(domain_id=5),  # Your domain ID
-    ]
-)
-```
-
-### Handling Different Action Dimensions
-
-When your dataset has fewer action dimensions than the pretrained model:
-
-**Option 1 (Recommended)**: Use `auto` action mode
-
-```bash
-# Automatically detects your dataset's action dimension
-# Works with any robot without custom code
-policy.action_mode=auto
-policy.max_action_dim=20  # Match pretrained model
-```
-
-**Option 2**: Use a predefined action mode with built-in padding
-
-```python
-# Model expects 20D, dataset has 12D
-# Action mode handles padding internally
-action_mode = "so101_bimanual"  # Pads 12 → 20
-```
-
-**Option 2**: Create a custom action mode that maps dimensions explicitly
-
-```python
-@register_action("my_mapped_action")
-class MappedActionSpace(BaseActionSpace):
-    dim_action = 20
-    REAL_DIM = 12
-
-    def _pad_to_model_dim(self, x):
-        # Custom padding logic
-        ...
-```
-
-## Troubleshooting
-
-### Common Issues
-
-**Issue**: "Action dimension mismatch"
-
-- **Solution**: Check that your `action_mode` matches your robot's action space. Create a custom action mode if needed.
-
-**Issue**: "Image values outside [0, 1] range"
-
-- **Solution**: Ensure images are preprocessed with `XVLAImageToFloatProcessorStep` before normalization.
-
-**Issue**: "Domain ID not found"
-
-- **Solution**: Make sure `XVLAAddDomainIdProcessorStep` is in your preprocessing pipeline with the correct domain_id.
-
-**Issue**: "Low success rate on new embodiment"
-
-- **Solution**:
-  1. Verify your action_mode is correct
-  2. Check that soft prompts are being trained (`train_soft_prompts=True`)
-  3. Ensure proper preprocessing (ImageNet normalization, domain_id)
-  4. Consider increasing training steps
-
-**Issue**: "Out of memory during training"
-
-- **Solution**:
-  1. Reduce `chunk_size` (e.g., from 32 to 16)
-  2. Enable gradient checkpointing
-  3. Reduce batch size
-  4. Freeze more components
-
-## Citation
-
-If you use X-VLA in your research, please cite:
-
-```bibtex
-@article{zheng2025x,
-  title   = {X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model},
-  author  = {Zheng, Jinliang and Li, Jianxiong and Wang, Zhihao and Liu, Dongxiu and Kang, Xirui
-             and Feng, Yuchun and Zheng, Yinan and Zou, Jiayin and Chen, Yilun and Zeng, Jia and others},
-  journal = {arXiv preprint arXiv:2510.10274},
-  year    = {2025}
-}
-```
-
-## Additional Resources
-
-- [X-VLA Paper](https://arxiv.org/pdf/2510.10274)
-- [LeRobot Documentation](https://github.com/huggingface/lerobot)
-- [Action Registry Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/action_hub.py)
-- [Processor Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/processor_xvla.py)
-- [Model Configuration](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/configuration_xvla.py)
-
-## Contributing
-
-We welcome contributions! If you've implemented a new action mode or processor for your robot, please consider submitting a PR to help the community.

examples/backward_compatibility/replay.py

@@ -45,7 +45,7 @@ from lerobot.robots import (  # noqa: F401
     so101_follower,
 )
 from lerobot.utils.constants import ACTION
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.utils import (
     init_logging,
     log_say,
@@ -97,7 +97,7 @@ def replay(cfg: ReplayConfig):
         robot.send_action(action)
 
         dt_s = time.perf_counter() - start_episode_t
-        precise_sleep(1 / dataset.fps - dt_s)
+        busy_wait(1 / dataset.fps - dt_s)
 
     robot.disconnect()
 

examples/dataset/load_lerobot_dataset.py

@@ -34,106 +34,105 @@ from huggingface_hub import HfApi
 import lerobot
 from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 
+# We ported a number of existing datasets ourselves, use this to see the list:
+print("List of available datasets:")
+pprint(lerobot.available_datasets)
 
-def main():
-    # We ported a number of existing datasets ourselves, use this to see the list:
-    print("List of available datasets:")
-    pprint(lerobot.available_datasets)
+# You can also browse through the datasets created/ported by the community on the hub using the hub api:
+hub_api = HfApi()
+repo_ids = [info.id for info in hub_api.list_datasets(task_categories="robotics", tags=["LeRobot"])]
+pprint(repo_ids)
 
-    # You can also browse through the datasets created/ported by the community on the hub using the hub api:
-    hub_api = HfApi()
-    repo_ids = [info.id for info in hub_api.list_datasets(task_categories="robotics", tags=["LeRobot"])]
-    pprint(repo_ids)
+# Or simply explore them in your web browser directly at:
+# https://huggingface.co/datasets?other=LeRobot
 
-    # Or simply explore them in your web browser directly at:
-    # https://huggingface.co/datasets?other=LeRobot
+# Let's take this one for this example
+repo_id = "lerobot/aloha_mobile_cabinet"
+# We can have a look and fetch its metadata to know more about it:
+ds_meta = LeRobotDatasetMetadata(repo_id)
 
-    # Let's take this one for this example
-    repo_id = "lerobot/aloha_mobile_cabinet"
-    # We can have a look and fetch its metadata to know more about it:
-    ds_meta = LeRobotDatasetMetadata(repo_id)
+# By instantiating just this class, you can quickly access useful information about the content and the
+# structure of the dataset without downloading the actual data yet (only metadata files — which are
+# lightweight).
+print(f"Total number of episodes: {ds_meta.total_episodes}")
+print(f"Average number of frames per episode: {ds_meta.total_frames / ds_meta.total_episodes:.3f}")
+print(f"Frames per second used during data collection: {ds_meta.fps}")
+print(f"Robot type: {ds_meta.robot_type}")
+print(f"keys to access images from cameras: {ds_meta.camera_keys=}\n")
 
-    # By instantiating just this class, you can quickly access useful information about the content and the
-    # structure of the dataset without downloading the actual data yet (only metadata files — which are
-    # lightweight).
-    print(f"Total number of episodes: {ds_meta.total_episodes}")
-    print(f"Average number of frames per episode: {ds_meta.total_frames / ds_meta.total_episodes:.3f}")
-    print(f"Frames per second used during data collection: {ds_meta.fps}")
-    print(f"Robot type: {ds_meta.robot_type}")
-    print(f"keys to access images from cameras: {ds_meta.camera_keys=}\n")
+print("Tasks:")
+print(ds_meta.tasks)
+print("Features:")
+pprint(ds_meta.features)
 
-    print("Tasks:")
-    print(ds_meta.tasks)
-    print("Features:")
-    pprint(ds_meta.features)
+# You can also get a short summary by simply printing the object:
+print(ds_meta)
 
-    # You can also get a short summary by simply printing the object:
-    print(ds_meta)
+# You can then load the actual dataset from the hub.
+# Either load any subset of episodes:
+dataset = LeRobotDataset(repo_id, episodes=[0, 10, 11, 23])
 
-    # You can then load the actual dataset from the hub.
-    # Either load any subset of episodes:
-    dataset = LeRobotDataset(repo_id, episodes=[0, 10, 11, 23])
+# And see how many frames you have:
+print(f"Selected episodes: {dataset.episodes}")
+print(f"Number of episodes selected: {dataset.num_episodes}")
+print(f"Number of frames selected: {dataset.num_frames}")
 
-    # And see how many frames you have:
-    print(f"Selected episodes: {dataset.episodes}")
-    print(f"Number of episodes selected: {dataset.num_episodes}")
-    print(f"Number of frames selected: {dataset.num_frames}")
+# Or simply load the entire dataset:
+dataset = LeRobotDataset(repo_id)
+print(f"Number of episodes selected: {dataset.num_episodes}")
+print(f"Number of frames selected: {dataset.num_frames}")
 
-    # Or simply load the entire dataset:
-    dataset = LeRobotDataset(repo_id)
-    print(f"Number of episodes selected: {dataset.num_episodes}")
-    print(f"Number of frames selected: {dataset.num_frames}")
+# The previous metadata class is contained in the 'meta' attribute of the dataset:
+print(dataset.meta)
 
-    # The previous metadata class is contained in the 'meta' attribute of the dataset:
-    print(dataset.meta)
+# LeRobotDataset actually wraps an underlying Hugging Face dataset
+# (see https://huggingface.co/docs/datasets for more information).
+print(dataset.hf_dataset)
 
-    # LeRobotDataset actually wraps an underlying Hugging Face dataset
-    # (see https://huggingface.co/docs/datasets for more information).
-    print(dataset.hf_dataset)
+# LeRobot datasets also subclasses PyTorch datasets so you can do everything you know and love from working
+# with the latter, like iterating through the dataset.
+# The __getitem__ iterates over the frames of the dataset. Since our datasets are also structured by
+# episodes, you can access the frame indices of any episode using dataset.meta.episodes. Here, we access
+# frame indices associated to the first episode:
+episode_index = 0
+from_idx = dataset.meta.episodes["dataset_from_index"][episode_index]
+to_idx = dataset.meta.episodes["dataset_to_index"][episode_index]
 
-    # LeRobot datasets also subclasses PyTorch datasets so you can do everything you know and love from working
-    # with the latter, like iterating through the dataset.
-    # The __getitem__ iterates over the frames of the dataset. Since our datasets are also structured by
-    # episodes, you can access the frame indices of any episode using dataset.meta.episodes. Here, we access
-    # frame indices associated to the first episode:
-    episode_index = 0
-    from_idx = dataset.meta.episodes["dataset_from_index"][episode_index]
-    to_idx = dataset.meta.episodes["dataset_to_index"][episode_index]
+# Then we grab all the image frames from the first camera:
+camera_key = dataset.meta.camera_keys[0]
+frames = [dataset[idx][camera_key] for idx in range(from_idx, to_idx)]
 
-    # Then we grab all the image frames from the first camera:
-    camera_key = dataset.meta.camera_keys[0]
-    frames = [dataset[idx][camera_key] for idx in range(from_idx, to_idx)]
+# The objects returned by the dataset are all torch.Tensors
+print(type(frames[0]))
+print(frames[0].shape)
 
-    # The objects returned by the dataset are all torch.Tensors
-    print(type(frames[0]))
-    print(frames[0].shape)
+# Since we're using pytorch, the shape is in pytorch, channel-first convention (c, h, w).
+# We can compare this shape with the information available for that feature
+pprint(dataset.features[camera_key])
+# In particular:
+print(dataset.features[camera_key]["shape"])
+# The shape is in (h, w, c) which is a more universal format.
 
-    # Since we're using pytorch, the shape is in pytorch, channel-first convention (c, h, w).
-    # We can compare this shape with the information available for that feature
-    pprint(dataset.features[camera_key])
-    # In particular:
-    print(dataset.features[camera_key]["shape"])
-    # The shape is in (h, w, c) which is a more universal format.
+# For many machine learning applications we need to load the history of past observations or trajectories of
+# future actions. Our datasets can load previous and future frames for each key/modality, using timestamps
+# differences with the current loaded frame. For instance:
+delta_timestamps = {
+    # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
+    camera_key: [-1, -0.5, -0.20, 0],
+    # loads 6 state vectors: 1.5 seconds before, 1 second before, ... 200 ms, 100 ms, and current frame
+    "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, 0],
+    # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
+    "action": [t / dataset.fps for t in range(64)],
+}
+# Note that in any case, these delta_timestamps values need to be multiples of (1/fps) so that added to any
+# timestamp, you still get a valid timestamp.
 
-    # For many machine learning applications we need to load the history of past observations or trajectories of
-    # future actions. Our datasets can load previous and future frames for each key/modality, using timestamps
-    # differences with the current loaded frame. For instance:
-    delta_timestamps = {
-        # loads 4 images: 1 second before current frame, 500 ms before, 200 ms before, and current frame
-        camera_key: [-1, -0.5, -0.20, 0],
-        # loads 6 state vectors: 1.5 seconds before, 1 second before, ... 200 ms, 100 ms, and current frame
-        "observation.state": [-1.5, -1, -0.5, -0.20, -0.10, 0],
-        # loads 64 action vectors: current frame, 1 frame in the future, 2 frames, ... 63 frames in the future
-        "action": [t / dataset.fps for t in range(64)],
-    }
-    # Note that in any case, these delta_timestamps values need to be multiples of (1/fps) so that added to any
-    # timestamp, you still get a valid timestamp.
-
-    dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
-    print(f"\n{dataset[0][camera_key].shape=}")  # (4, c, h, w)
-    print(f"{dataset[0]['observation.state'].shape=}")  # (6, c)
-    print(f"{dataset[0]['action'].shape=}\n")  # (64, c)
+dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps)
+print(f"\n{dataset[0][camera_key].shape=}")  # (4, c, h, w)
+print(f"{dataset[0]['observation.state'].shape=}")  # (6, c)
+print(f"{dataset[0]['action'].shape=}\n")  # (64, c)
 
+if __name__ == "__main__":
     dataloader = torch.utils.data.DataLoader(
         dataset,
         num_workers=4,
@@ -145,7 +144,3 @@ def main():
         print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
         print(f"{batch['action'].shape=}")  # (32, 64, c)
         break
-
-
-if __name__ == "__main__":
-    main()

examples/dataset_annotation/visualize_subtask_annotations.py

@@ -0,0 +1,525 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Visualize SARM Subtask Annotations
+
+This script creates visualizations of the subtask annotations generated by subtask_annotation.py.
+For each episode, it shows:
+- A timeline with dashed vertical lines at subtask boundaries
+- Sample frames from the episode at key points (start, middle, end of each subtask)
+- Color-coded subtask segments
+
+Usage:
+    python visualize_subtask_annotations.py --repo-id pepijn223/mydataset --video-key observation.images.top --num-episodes 5
+"""
+
+import argparse
+import random
+from pathlib import Path
+
+import cv2
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import numpy as np
+import pandas as pd
+from matplotlib.lines import Line2D
+from rich.console import Console
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import load_episodes
+from lerobot.policies.sarm.sarm_utils import SubtaskAnnotation, Subtask, Timestamp
+
+
+def timestamp_to_seconds(timestamp: str) -> float:
+    """Convert MM:SS or SS timestamp to seconds"""
+    parts = timestamp.split(":")
+    if len(parts) == 2:
+        return int(parts[0]) * 60 + int(parts[1])
+    else:
+        return int(parts[0])
+
+
+def load_annotations_from_dataset(dataset_path: Path) -> dict[int, SubtaskAnnotation]:
+    """
+    Load annotations from LeRobot dataset parquet files.
+    
+    Reads subtask annotations from the episodes metadata parquet files.
+    """
+    episodes_dataset = load_episodes(dataset_path)
+    
+    if episodes_dataset is None or len(episodes_dataset) == 0:
+        return {}
+    
+    # Check if subtask columns exist
+    if "subtask_names" not in episodes_dataset.column_names:
+        return {}
+    
+    # Convert to pandas DataFrame for easier access
+    episodes_df = episodes_dataset.to_pandas()
+    
+    annotations = {}
+    
+    for ep_idx in episodes_df.index:
+        subtask_names = episodes_df.loc[ep_idx, "subtask_names"]
+        
+        # Skip episodes without annotations
+        if subtask_names is None or (isinstance(subtask_names, float) and pd.isna(subtask_names)):
+            continue
+        
+        start_times = episodes_df.loc[ep_idx, "subtask_start_times"]
+        end_times = episodes_df.loc[ep_idx, "subtask_end_times"]
+        
+        # Reconstruct SubtaskAnnotation from stored data
+        subtasks = []
+        for i, name in enumerate(subtask_names):
+            # Convert seconds back to MM:SS format
+            start_sec = int(start_times[i])
+            end_sec = int(end_times[i])
+            start_str = f"{start_sec // 60:02d}:{start_sec % 60:02d}"
+            end_str = f"{end_sec // 60:02d}:{end_sec % 60:02d}"
+            
+            subtasks.append(
+                Subtask(
+                    name=name,
+                    timestamps=Timestamp(start=start_str, end=end_str)
+                )
+            )
+        
+        annotations[int(ep_idx)] = SubtaskAnnotation(subtasks=subtasks)
+    
+    return annotations
+
+
+# Color palette for subtasks (colorblind-friendly)
+SUBTASK_COLORS = [
+    "#E69F00",  # Orange
+    "#56B4E9",  # Sky blue
+    "#009E73",  # Bluish green
+    "#F0E442",  # Yellow
+    "#0072B2",  # Blue
+    "#D55E00",  # Vermillion
+    "#CC79A7",  # Reddish purple
+    "#999999",  # Gray
+]
+
+
+def extract_frame_from_video(video_path: Path, timestamp: float) -> np.ndarray | None:
+    """Extract a single frame from video at given timestamp."""
+    cap = cv2.VideoCapture(str(video_path))
+    if not cap.isOpened():
+        return None
+    
+    # Set position to timestamp
+    cap.set(cv2.CAP_PROP_POS_MSEC, timestamp * 1000)
+    ret, frame = cap.read()
+    cap.release()
+    
+    if ret:
+        # Convert BGR to RGB
+        return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    return None
+
+
+def visualize_episode(
+    episode_idx: int,
+    annotation,
+    video_path: Path,
+    video_start_timestamp: float,
+    video_end_timestamp: float,
+    fps: int,
+    output_path: Path,
+    video_key: str,
+):
+    """
+    Create visualization for a single episode.
+    
+    Shows:
+    - Top row: Sample frames from the episode (one per subtask)
+    - Bottom: Timeline with subtask segments and boundary lines
+    """
+    subtasks = annotation.subtasks
+    num_subtasks = len(subtasks)
+    
+    if num_subtasks == 0:
+        print(f"No subtasks found for episode {episode_idx}")
+        return
+    
+    # Calculate episode duration
+    episode_duration = video_end_timestamp - video_start_timestamp
+    
+    # Extract sample frames - get frame from middle of each subtask
+    sample_frames = []
+    frame_timestamps = []
+    
+    for subtask in subtasks:
+        start_sec = timestamp_to_seconds(subtask.timestamps.start)
+        end_sec = timestamp_to_seconds(subtask.timestamps.end)
+        mid_sec = (start_sec + end_sec) / 2
+        
+        # Convert to video timestamp (add video_start_timestamp offset)
+        video_timestamp = video_start_timestamp + mid_sec
+        frame_timestamps.append(mid_sec)
+        
+        frame = extract_frame_from_video(video_path, video_timestamp)
+        sample_frames.append(frame)
+    
+    # Create figure
+    fig = plt.figure(figsize=(16, 10))
+    
+    # Use a dark background for better contrast
+    fig.patch.set_facecolor('#1a1a2e')
+    
+    # Calculate grid layout
+    # Top section: frames (variable number of columns based on subtasks)
+    # Bottom section: timeline
+    
+    # Create gridspec
+    gs = fig.add_gridspec(
+        2, max(num_subtasks, 1), 
+        height_ratios=[2, 1],
+        hspace=0.3,
+        wspace=0.1,
+        left=0.05, right=0.95,
+        top=0.88, bottom=0.1
+    )
+    
+    # Add title
+    fig.suptitle(
+        f"Episode {episode_idx} - Subtask Annotations",
+        fontsize=18,
+        fontweight='bold',
+        color='white',
+        y=0.96
+    )
+    
+    # Add subtitle with video info
+    fig.text(
+        0.5, 0.91,
+        f"Camera: {video_key} | Duration: {episode_duration:.1f}s | {num_subtasks} subtasks",
+        ha='center',
+        fontsize=11,
+        color='#888888'
+    )
+    
+    # Plot sample frames
+    for i, (frame, subtask) in enumerate(zip(sample_frames, subtasks)):
+        ax = fig.add_subplot(gs[0, i])
+        ax.set_facecolor('#16213e')
+        
+        if frame is not None:
+            ax.imshow(frame)
+        else:
+            ax.text(0.5, 0.5, "Frame\nN/A", ha='center', va='center', 
+                   fontsize=12, color='white', transform=ax.transAxes)
+        
+        ax.set_title(
+            f"{subtask.name}",
+            fontsize=10,
+            fontweight='bold',
+            color=SUBTASK_COLORS[i % len(SUBTASK_COLORS)],
+            pad=8
+        )
+        ax.axis('off')
+        
+        # Add frame timestamp below
+        ax.text(
+            0.5, -0.08,
+            f"t={frame_timestamps[i]:.1f}s",
+            ha='center',
+            fontsize=9,
+            color='#888888',
+            transform=ax.transAxes
+        )
+    
+    # Create timeline subplot spanning all columns
+    ax_timeline = fig.add_subplot(gs[1, :])
+    ax_timeline.set_facecolor('#16213e')
+    
+    # Get total duration from last subtask end time
+    total_duration = timestamp_to_seconds(subtasks[-1].timestamps.end)
+    
+    # Draw subtask segments as colored bars
+    bar_height = 0.6
+    bar_y = 0.5
+    
+    for i, subtask in enumerate(subtasks):
+        start_sec = timestamp_to_seconds(subtask.timestamps.start)
+        end_sec = timestamp_to_seconds(subtask.timestamps.end)
+        color = SUBTASK_COLORS[i % len(SUBTASK_COLORS)]
+        
+        # Draw segment bar
+        rect = mpatches.FancyBboxPatch(
+            (start_sec, bar_y - bar_height/2),
+            end_sec - start_sec,
+            bar_height,
+            boxstyle="round,pad=0.02,rounding_size=0.1",
+            facecolor=color,
+            edgecolor='white',
+            linewidth=1.5,
+            alpha=0.85
+        )
+        ax_timeline.add_patch(rect)
+        
+        # Add subtask label inside bar
+        mid_x = (start_sec + end_sec) / 2
+        duration = end_sec - start_sec
+        
+        # Only add text if segment is wide enough
+        if duration > total_duration * 0.08:
+            ax_timeline.text(
+                mid_x, bar_y,
+                subtask.name,
+                ha='center', va='center',
+                fontsize=9,
+                fontweight='bold',
+                color='black' if i in [3] else 'white',  # Yellow needs dark text
+                rotation=0 if duration > total_duration * 0.15 else 45
+            )
+    
+    # Draw boundary lines (dashed vertical lines between subtasks)
+    boundary_times = []
+    for i, subtask in enumerate(subtasks):
+        start_sec = timestamp_to_seconds(subtask.timestamps.start)
+        end_sec = timestamp_to_seconds(subtask.timestamps.end)
+        
+        # Add start boundary (except for first subtask at t=0)
+        if i == 0 and start_sec > 0:
+            boundary_times.append(start_sec)
+        elif i > 0:
+            boundary_times.append(start_sec)
+        
+        # Add end boundary for last subtask
+        if i == len(subtasks) - 1:
+            boundary_times.append(end_sec)
+    
+    # Draw dashed lines at boundaries
+    for t in boundary_times:
+        ax_timeline.axvline(
+            x=t, 
+            ymin=0.1, ymax=0.9,
+            color='white', 
+            linestyle='--', 
+            linewidth=2,
+            alpha=0.9
+        )
+        
+        # Add time label below line
+        ax_timeline.text(
+            t, 0.0,
+            f"{int(t//60):02d}:{int(t%60):02d}",
+            ha='center', va='top',
+            fontsize=8,
+            color='#cccccc'
+        )
+    
+    # Add start line at t=0
+    ax_timeline.axvline(x=0, ymin=0.1, ymax=0.9, color='#00ff00', linestyle='-', linewidth=2.5, alpha=0.9)
+    ax_timeline.text(0, 0.0, "00:00", ha='center', va='top', fontsize=8, color='#00ff00', fontweight='bold')
+    
+    # Configure timeline axes
+    ax_timeline.set_xlim(-total_duration * 0.02, total_duration * 1.02)
+    ax_timeline.set_ylim(-0.3, 1.2)
+    ax_timeline.set_xlabel("Time (seconds)", fontsize=11, color='white', labelpad=10)
+    ax_timeline.set_ylabel("")
+    
+    # Style the axes
+    ax_timeline.spines['top'].set_visible(False)
+    ax_timeline.spines['right'].set_visible(False)
+    ax_timeline.spines['left'].set_visible(False)
+    ax_timeline.spines['bottom'].set_color('#444444')
+    ax_timeline.tick_params(axis='x', colors='#888888', labelsize=9)
+    ax_timeline.tick_params(axis='y', left=False, labelleft=False)
+    
+    # Add x-axis ticks at regular intervals
+    tick_interval = max(1, int(total_duration / 10))
+    ax_timeline.set_xticks(np.arange(0, total_duration + tick_interval, tick_interval))
+    
+    # Add legend explaining line styles
+    legend_elements = [
+        Line2D([0], [0], color='#00ff00', linewidth=2.5, linestyle='-', label='Start'),
+        Line2D([0], [0], color='white', linewidth=2, linestyle='--', label='Subtask boundary'),
+    ]
+    ax_timeline.legend(
+        handles=legend_elements, 
+        loc='upper right',
+        framealpha=0.3,
+        facecolor='#16213e',
+        edgecolor='#444444',
+        fontsize=9,
+        labelcolor='white'
+    )
+    
+    # Save figure
+    plt.savefig(output_path, dpi=150, facecolor=fig.get_facecolor(), edgecolor='none', bbox_inches='tight')
+    plt.close()
+    
+    return output_path
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Visualize SARM subtask annotations",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="HuggingFace dataset repository ID",
+    )
+    parser.add_argument(
+        "--num-episodes",
+        type=int,
+        default=5,
+        help="Number of random episodes to visualize (default: 5)",
+    )
+    parser.add_argument(
+        "--episodes",
+        type=int,
+        nargs="+",
+        default=None,
+        help="Specific episode indices to visualize (overrides --num-episodes)",
+    )
+    parser.add_argument(
+        "--video-key",
+        type=str,
+        default=None,
+        help="Camera/video key to use. If not specified, uses first available.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="./subtask_viz",
+        help="Output directory for visualizations (default: ./subtask_viz)",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=None,
+        help="Random seed for reproducibility",
+    )
+    
+    args = parser.parse_args()
+    
+    console = Console()
+    
+    # Set random seed if specified
+    if args.seed is not None:
+        random.seed(args.seed)
+    
+    console.print(f"\n[cyan]Loading dataset: {args.repo_id}[/cyan]")
+    dataset = LeRobotDataset(args.repo_id, download_videos=True)
+    fps = dataset.fps
+    
+    # Get video key
+    if args.video_key:
+        if args.video_key not in dataset.meta.video_keys:
+            console.print(f"[red]Error: Video key '{args.video_key}' not found[/red]")
+            console.print(f"[yellow]Available: {', '.join(dataset.meta.video_keys)}[/yellow]")
+            return
+        video_key = args.video_key
+    else:
+        video_key = dataset.meta.video_keys[0]
+    
+    console.print(f"[cyan]Using camera: {video_key}[/cyan]")
+    console.print(f"[cyan]FPS: {fps}[/cyan]")
+    
+    # Load annotations
+    console.print(f"\n[cyan]Loading annotations...[/cyan]")
+    annotations = load_annotations_from_dataset(dataset.root)
+    
+    if not annotations:
+        console.print("[red]Error: No annotations found in dataset[/red]")
+        console.print("[yellow]Run subtask_annotation.py first to generate annotations[/yellow]")
+        return
+    
+    console.print(f"[green]Found {len(annotations)} annotated episodes[/green]")
+    
+    # Determine which episodes to visualize
+    if args.episodes:
+        episode_indices = args.episodes
+        # Validate episodes exist
+        for ep in episode_indices:
+            if ep not in annotations:
+                console.print(f"[yellow]Warning: Episode {ep} has no annotation, skipping[/yellow]")
+        episode_indices = [ep for ep in episode_indices if ep in annotations]
+    else:
+        # Random selection
+        available_episodes = list(annotations.keys())
+        num_to_select = min(args.num_episodes, len(available_episodes))
+        episode_indices = random.sample(available_episodes, num_to_select)
+        episode_indices.sort()
+    
+    if not episode_indices:
+        console.print("[red]Error: No valid episodes to visualize[/red]")
+        return
+    
+    console.print(f"[cyan]Visualizing episodes: {episode_indices}[/cyan]")
+    
+    # Create output directory
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Generate visualizations
+    for ep_idx in episode_indices:
+        console.print(f"\n[cyan]Processing episode {ep_idx}...[/cyan]")
+        
+        annotation = annotations[ep_idx]
+        
+        # Get video path and timestamps
+        video_path = dataset.root / dataset.meta.get_video_file_path(ep_idx, video_key)
+        
+        if not video_path.exists():
+            console.print(f"[red]Video not found: {video_path}[/red]")
+            continue
+        
+        # Get episode-specific timestamps within the video file
+        video_path_key = f"videos/{video_key}/from_timestamp"
+        video_path_key_to = f"videos/{video_key}/to_timestamp"
+        
+        video_start_timestamp = float(dataset.meta.episodes[video_path_key][ep_idx])
+        video_end_timestamp = float(dataset.meta.episodes[video_path_key_to][ep_idx])
+        
+        # Create visualization
+        output_path = output_dir / f"episode_{ep_idx:04d}_subtasks.png"
+        
+        try:
+            visualize_episode(
+                episode_idx=ep_idx,
+                annotation=annotation,
+                video_path=video_path,
+                video_start_timestamp=video_start_timestamp,
+                video_end_timestamp=video_end_timestamp,
+                fps=fps,
+                output_path=output_path,
+                video_key=video_key,
+            )
+            console.print(f"[green]✓ Saved: {output_path}[/green]")
+        except Exception as e:
+            console.print(f"[red]✗ Failed to visualize episode {ep_idx}: {e}[/red]")
+    
+    # Print summary
+    console.print(f"\n[bold green]{'=' * 50}[/bold green]")
+    console.print(f"[bold green]Visualization Complete![/bold green]")
+    console.print(f"[bold green]{'=' * 50}[/bold green]")
+    console.print(f"Output directory: {output_dir.absolute()}")
+    console.print(f"Episodes visualized: {len(episode_indices)}")
+
+
+if __name__ == "__main__":
+    main()
+

examples/lekiwi/evaluate.py

@@ -33,68 +33,83 @@ TASK_DESCRIPTION = "My task description"
 HF_MODEL_ID = "<hf_username>/<model_repo_id>"
 HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
 
+# Create the robot configuration & robot
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 
-def main():
-    # Create the robot configuration & robot
-    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+robot = LeKiwiClient(robot_config)
 
-    robot = LeKiwiClient(robot_config)
+# Create policy
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
 
-    # Create policy
-    policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+# Configure the dataset features
+action_features = hw_to_dataset_features(robot.action_features, ACTION)
+obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
+dataset_features = {**action_features, **obs_features}
 
-    # Configure the dataset features
-    action_features = hw_to_dataset_features(robot.action_features, ACTION)
-    obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
-    dataset_features = {**action_features, **obs_features}
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_DATASET_ID,
+    fps=FPS,
+    features=dataset_features,
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
 
-    # Create the dataset
-    dataset = LeRobotDataset.create(
-        repo_id=HF_DATASET_ID,
+# Build Policy Processors
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=policy,
+    pretrained_path=HF_MODEL_ID,
+    dataset_stats=dataset.meta.stats,
+    # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
+    preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
+)
+
+# Connect the robot
+# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
+robot.connect()
+
+# TODO(Steven): Update this example to use pipelines
+teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="lekiwi_evaluate")
+
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
+
+print("Starting evaluate loop...")
+recorded_episodes = 0
+while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
+    log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
         fps=FPS,
-        features=dataset_features,
-        robot_type=robot.name,
-        use_videos=True,
-        image_writer_threads=4,
+        policy=policy,
+        preprocessor=preprocessor,  # Pass the pre and post policy processors
+        postprocessor=postprocessor,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=teleop_action_processor,
+        robot_action_processor=robot_action_processor,
+        robot_observation_processor=robot_observation_processor,
     )
 
-    # Build Policy Processors
-    preprocessor, postprocessor = make_pre_post_processors(
-        policy_cfg=policy,
-        pretrained_path=HF_MODEL_ID,
-        dataset_stats=dataset.meta.stats,
-        # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
-        preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
-    )
-
-    # Connect the robot
-    # To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
-    robot.connect()
-
-    # TODO(Steven): Update this example to use pipelines
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
-
-    # Initialize the keyboard listener and rerun visualization
-    listener, events = init_keyboard_listener()
-    init_rerun(session_name="lekiwi_evaluate")
-
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
-
-    print("Starting evaluate loop...")
-    recorded_episodes = 0
-    while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-        log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
-
-        # Main record loop
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (
+        (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
+    ):
+        log_say("Reset the environment")
         record_loop(
             robot=robot,
             events=events,
             fps=FPS,
-            policy=policy,
-            preprocessor=preprocessor,  # Pass the pre and post policy processors
-            postprocessor=postprocessor,
-            dataset=dataset,
             control_time_s=EPISODE_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
@@ -103,42 +118,21 @@ def main():
             robot_observation_processor=robot_observation_processor,
         )
 
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and (
-            (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
-        ):
-            log_say("Reset the environment")
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
-            )
+    if events["rerecord_episode"]:
+        log_say("Re-record episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
 
-        if events["rerecord_episode"]:
-            log_say("Re-record episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+    # Save episode
+    dataset.save_episode()
+    recorded_episodes += 1
 
-        # Save episode
-        dataset.save_episode()
-        recorded_episodes += 1
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+listener.stop()
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    listener.stop()
-
-    dataset.finalize()
-    dataset.push_to_hub()
-
-
-if __name__ == "__main__":
-    main()
+dataset.finalize()
+dataset.push_to_hub()

examples/lekiwi/record.py

@@ -34,62 +34,78 @@ RESET_TIME_SEC = 10
 TASK_DESCRIPTION = "My task description"
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+# Create the robot and teleoperator configurations
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
+keyboard_config = KeyboardTeleopConfig()
 
-def main():
-    # Create the robot and teleoperator configurations
-    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
-    leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
-    keyboard_config = KeyboardTeleopConfig()
+# Initialize the robot and teleoperator
+robot = LeKiwiClient(robot_config)
+leader_arm = SO100Leader(leader_arm_config)
+keyboard = KeyboardTeleop(keyboard_config)
 
-    # Initialize the robot and teleoperator
-    robot = LeKiwiClient(robot_config)
-    leader_arm = SO100Leader(leader_arm_config)
-    keyboard = KeyboardTeleop(keyboard_config)
+# TODO(Steven): Update this example to use pipelines
+teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
 
-    # TODO(Steven): Update this example to use pipelines
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+# Configure the dataset features
+action_features = hw_to_dataset_features(robot.action_features, ACTION)
+obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
+dataset_features = {**action_features, **obs_features}
 
-    # Configure the dataset features
-    action_features = hw_to_dataset_features(robot.action_features, ACTION)
-    obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
-    dataset_features = {**action_features, **obs_features}
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_REPO_ID,
+    fps=FPS,
+    features=dataset_features,
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
 
-    # Create the dataset
-    dataset = LeRobotDataset.create(
-        repo_id=HF_REPO_ID,
+# Connect the robot and teleoperator
+# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
+robot.connect()
+leader_arm.connect()
+keyboard.connect()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="lekiwi_record")
+
+if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
+
+print("Starting record loop...")
+recorded_episodes = 0
+while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
+    log_say(f"Recording episode {recorded_episodes}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
         fps=FPS,
-        features=dataset_features,
-        robot_type=robot.name,
-        use_videos=True,
-        image_writer_threads=4,
+        dataset=dataset,
+        teleop=[leader_arm, keyboard],
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=teleop_action_processor,
+        robot_action_processor=robot_action_processor,
+        robot_observation_processor=robot_observation_processor,
     )
 
-    # Connect the robot and teleoperator
-    # To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
-    robot.connect()
-    leader_arm.connect()
-    keyboard.connect()
-
-    # Initialize the keyboard listener and rerun visualization
-    listener, events = init_keyboard_listener()
-    init_rerun(session_name="lekiwi_record")
-
-    if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-        raise ValueError("Robot or teleop is not connected!")
-
-    print("Starting record loop...")
-    recorded_episodes = 0
-    while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-        log_say(f"Recording episode {recorded_episodes}")
-
-        # Main record loop
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (
+        (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
+    ):
+        log_say("Reset the environment")
         record_loop(
             robot=robot,
             events=events,
             fps=FPS,
-            dataset=dataset,
             teleop=[leader_arm, keyboard],
-            control_time_s=EPISODE_TIME_SEC,
+            control_time_s=RESET_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
             teleop_action_processor=teleop_action_processor,
@@ -97,45 +113,23 @@ def main():
             robot_observation_processor=robot_observation_processor,
         )
 
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and (
-            (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
-        ):
-            log_say("Reset the environment")
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                teleop=[leader_arm, keyboard],
-                control_time_s=RESET_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
-            )
+    if events["rerecord_episode"]:
+        log_say("Re-record episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
 
-        if events["rerecord_episode"]:
-            log_say("Re-record episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+    # Save episode
+    dataset.save_episode()
+    recorded_episodes += 1
 
-        # Save episode
-        dataset.save_episode()
-        recorded_episodes += 1
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+leader_arm.disconnect()
+keyboard.disconnect()
+listener.stop()
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    leader_arm.disconnect()
-    keyboard.disconnect()
-    listener.stop()
-
-    dataset.finalize()
-    dataset.push_to_hub()
-
-
-if __name__ == "__main__":
-    main()
+dataset.finalize()
+dataset.push_to_hub()

examples/lekiwi/replay.py

@@ -20,48 +20,42 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
 from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
 from lerobot.utils.constants import ACTION
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.utils import log_say
 
 EPISODE_IDX = 0
 
+# Initialize the robot config
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 
-def main():
-    # Initialize the robot config
-    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
+# Initialize the robot
+robot = LeKiwiClient(robot_config)
 
-    # Initialize the robot
-    robot = LeKiwiClient(robot_config)
+# Fetch the dataset to replay
+dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[EPISODE_IDX])
+# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
+episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
+actions = episode_frames.select_columns(ACTION)
 
-    # Fetch the dataset to replay
-    dataset = LeRobotDataset("<hf_username>/<dataset_repo_id>", episodes=[EPISODE_IDX])
-    # Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
-    episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
-    actions = episode_frames.select_columns(ACTION)
+# Connect to the robot
+robot.connect()
 
-    # Connect to the robot
-    robot.connect()
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+print("Starting replay loop...")
+log_say(f"Replaying episode {EPISODE_IDX}")
+for idx in range(len(episode_frames)):
+    t0 = time.perf_counter()
 
-    print("Starting replay loop...")
-    log_say(f"Replaying episode {EPISODE_IDX}")
-    for idx in range(len(episode_frames)):
-        t0 = time.perf_counter()
+    # Get recorded action from dataset
+    action = {
+        name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+    }
 
-        # Get recorded action from dataset
-        action = {
-            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-        }
+    # Send action to robot
+    _ = robot.send_action(action)
 
-        # Send action to robot
-        _ = robot.send_action(action)
+    busy_wait(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
 
-        precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
-
-    robot.disconnect()
-
-
-if __name__ == "__main__":
-    main()
+robot.disconnect()

examples/lekiwi/teleoperate.py

@@ -19,60 +19,54 @@ import time
 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
 from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig
 from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 FPS = 30
 
+# Create the robot and teleoperator configurations
+robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="my_lekiwi")
+teleop_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
+keyboard_config = KeyboardTeleopConfig(id="my_laptop_keyboard")
 
-def main():
-    # Create the robot and teleoperator configurations
-    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="my_lekiwi")
-    teleop_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
-    keyboard_config = KeyboardTeleopConfig(id="my_laptop_keyboard")
+# Initialize the robot and teleoperator
+robot = LeKiwiClient(robot_config)
+leader_arm = SO100Leader(teleop_arm_config)
+keyboard = KeyboardTeleop(keyboard_config)
 
-    # Initialize the robot and teleoperator
-    robot = LeKiwiClient(robot_config)
-    leader_arm = SO100Leader(teleop_arm_config)
-    keyboard = KeyboardTeleop(keyboard_config)
+# Connect to the robot and teleoperator
+# To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
+robot.connect()
+leader_arm.connect()
+keyboard.connect()
 
-    # Connect to the robot and teleoperator
-    # To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
-    robot.connect()
-    leader_arm.connect()
-    keyboard.connect()
+# Init rerun viewer
+init_rerun(session_name="lekiwi_teleop")
 
-    # Init rerun viewer
-    init_rerun(session_name="lekiwi_teleop")
+if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
 
-    if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-        raise ValueError("Robot or teleop is not connected!")
+print("Starting teleop loop...")
+while True:
+    t0 = time.perf_counter()
 
-    print("Starting teleop loop...")
-    while True:
-        t0 = time.perf_counter()
+    # Get robot observation
+    observation = robot.get_observation()
 
-        # Get robot observation
-        observation = robot.get_observation()
+    # Get teleop action
+    # Arm
+    arm_action = leader_arm.get_action()
+    arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
+    # Keyboard
+    keyboard_keys = keyboard.get_action()
+    base_action = robot._from_keyboard_to_base_action(keyboard_keys)
 
-        # Get teleop action
-        # Arm
-        arm_action = leader_arm.get_action()
-        arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
-        # Keyboard
-        keyboard_keys = keyboard.get_action()
-        base_action = robot._from_keyboard_to_base_action(keyboard_keys)
+    action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
 
-        action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
+    # Send action to robot
+    _ = robot.send_action(action)
 
-        # Send action to robot
-        _ = robot.send_action(action)
+    # Visualize
+    log_rerun_data(observation=observation, action=action)
 
-        # Visualize
-        log_rerun_data(observation=observation, action=action)
-
-        precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
-
-
-if __name__ == "__main__":
-    main()
+    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))

examples/phone_to_so100/evaluate.py

@@ -52,114 +52,125 @@ TASK_DESCRIPTION = "My task description"
 HF_MODEL_ID = "<hf_username>/<model_repo_id>"
 HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
+# Create the robot configuration & robot
+camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem58760434471",
+    id="my_awesome_follower_arm",
+    cameras=camera_config,
+    use_degrees=True,
+)
 
-def main():
-    # Create the robot configuration & robot
-    camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
-    robot_config = SO100FollowerConfig(
-        port="/dev/tty.usbmodem58760434471",
-        id="my_awesome_follower_arm",
-        cameras=camera_config,
-        use_degrees=True,
-    )
+robot = SO100Follower(robot_config)
 
-    robot = SO100Follower(robot_config)
+# Create policy
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
 
-    # Create policy
-    policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
-    )
-
-    # Build pipeline to convert EE action to joints action
-    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Build pipeline to convert joints observation to EE observation
-    robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
-            )
-        ],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-
-    # Create the dataset
-    dataset = LeRobotDataset.create(
-        repo_id=HF_DATASET_ID,
-        fps=FPS,
-        features=combine_feature_dicts(
-            aggregate_pipeline_dataset_features(
-                pipeline=robot_joints_to_ee_pose_processor,
-                initial_features=create_initial_features(observation=robot.observation_features),
-                use_videos=True,
-            ),
-            # User for now should be explicit on the feature keys that were used for record
-            # Alternatively, the user can pass the processor step that has the right features
-            aggregate_pipeline_dataset_features(
-                pipeline=make_default_teleop_action_processor(),
-                initial_features=create_initial_features(
-                    action={
-                        f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
-                        for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
-                    }
-                ),
-                use_videos=True,
-            ),
+# Build pipeline to convert EE action to joints action
+robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
         ),
-        robot_type=robot.name,
-        use_videos=True,
-        image_writer_threads=4,
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Build pipeline to convert joints observation to EE observation
+robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
+    steps=[
+        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+    ],
+    to_transition=observation_to_transition,
+    to_output=transition_to_observation,
+)
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_DATASET_ID,
+    fps=FPS,
+    features=combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_joints_to_ee_pose_processor,
+            initial_features=create_initial_features(observation=robot.observation_features),
+            use_videos=True,
+        ),
+        # User for now should be explicit on the feature keys that were used for record
+        # Alternatively, the user can pass the processor step that has the right features
+        aggregate_pipeline_dataset_features(
+            pipeline=make_default_teleop_action_processor(),
+            initial_features=create_initial_features(
+                action={
+                    f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
+                    for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
+                }
+            ),
+            use_videos=True,
+        ),
+    ),
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
+
+# Build Policy Processors
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=policy,
+    pretrained_path=HF_MODEL_ID,
+    dataset_stats=dataset.meta.stats,
+    # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
+    preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
+)
+
+# Connect the robot
+robot.connect()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="phone_so100_evaluate")
+
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
+
+print("Starting evaluate loop...")
+episode_idx = 0
+for episode_idx in range(NUM_EPISODES):
+    log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        policy=policy,
+        preprocessor=preprocessor,  # Pass the pre and post policy processors
+        postprocessor=postprocessor,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=make_default_teleop_action_processor(),
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose_processor,
     )
 
-    # Build Policy Processors
-    preprocessor, postprocessor = make_pre_post_processors(
-        policy_cfg=policy,
-        pretrained_path=HF_MODEL_ID,
-        dataset_stats=dataset.meta.stats,
-        # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
-        preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
-    )
-
-    # Connect the robot
-    robot.connect()
-
-    # Initialize the keyboard listener and rerun visualization
-    listener, events = init_keyboard_listener()
-    init_rerun(session_name="phone_so100_evaluate")
-
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
-
-    print("Starting evaluate loop...")
-    episode_idx = 0
-    for episode_idx in range(NUM_EPISODES):
-        log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
-
-        # Main record loop
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
+        log_say("Reset the environment")
         record_loop(
             robot=robot,
             events=events,
             fps=FPS,
-            policy=policy,
-            preprocessor=preprocessor,  # Pass the pre and post policy processors
-            postprocessor=postprocessor,
-            dataset=dataset,
             control_time_s=EPISODE_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
@@ -168,40 +179,21 @@ def main():
             robot_observation_processor=robot_joints_to_ee_pose_processor,
         )
 
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
-            log_say("Reset the environment")
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=make_default_teleop_action_processor(),
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose_processor,
-            )
+    if events["rerecord_episode"]:
+        log_say("Re-record episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
 
-        if events["rerecord_episode"]:
-            log_say("Re-record episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+    # Save episode
+    dataset.save_episode()
+    episode_idx += 1
 
-        # Save episode
-        dataset.save_episode()
-        episode_idx += 1
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+listener.stop()
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    listener.stop()
-
-    dataset.finalize()
-    dataset.push_to_hub()
-
-
-if __name__ == "__main__":
-    main()
+dataset.finalize()
+dataset.push_to_hub()

examples/phone_to_so100/record.py

@@ -50,122 +50,133 @@ RESET_TIME_SEC = 30
 TASK_DESCRIPTION = "My task description"
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+# Create the robot and teleoperator configurations
+camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411",
+    id="my_awesome_follower_arm",
+    cameras=camera_config,
+    use_degrees=True,
+)
+teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
 
-def main():
-    # Create the robot and teleoperator configurations
-    camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
-    robot_config = SO100FollowerConfig(
-        port="/dev/tty.usbmodem5A460814411",
-        id="my_awesome_follower_arm",
-        cameras=camera_config,
-        use_degrees=True,
-    )
-    teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
+# Initialize the robot and teleoperator
+robot = SO100Follower(robot_config)
+phone = Phone(teleop_config)
 
-    # Initialize the robot and teleoperator
-    robot = SO100Follower(robot_config)
-    phone = Phone(teleop_config)
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
-    )
-
-    # Build pipeline to convert phone action to EE action
-    phone_to_robot_ee_pose_processor = RobotProcessorPipeline[
-        tuple[RobotAction, RobotObservation], RobotAction
-    ](
-        steps=[
-            MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
-            EEReferenceAndDelta(
-                kinematics=kinematics_solver,
-                end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
-                motor_names=list(robot.bus.motors.keys()),
-                use_latched_reference=True,
-            ),
-            EEBoundsAndSafety(
-                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
-                max_ee_step_m=0.20,
-            ),
-            GripperVelocityToJoint(speed_factor=20.0),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Build pipeline to convert EE action to joints action
-    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Build pipeline to convert joint observation to EE observation
-    robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
-            )
-        ],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-
-    # Create the dataset
-    dataset = LeRobotDataset.create(
-        repo_id=HF_REPO_ID,
-        fps=FPS,
-        features=combine_feature_dicts(
-            # Run the feature contract of the pipelines
-            # This tells you how the features would look like after the pipeline steps
-            aggregate_pipeline_dataset_features(
-                pipeline=phone_to_robot_ee_pose_processor,
-                initial_features=create_initial_features(action=phone.action_features),
-                use_videos=True,
-            ),
-            aggregate_pipeline_dataset_features(
-                pipeline=robot_joints_to_ee_pose,
-                initial_features=create_initial_features(observation=robot.observation_features),
-                use_videos=True,
-            ),
+# Build pipeline to convert phone action to EE action
+phone_to_robot_ee_pose_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
+        EEReferenceAndDelta(
+            kinematics=kinematics_solver,
+            end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+            motor_names=list(robot.bus.motors.keys()),
+            use_latched_reference=True,
         ),
-        robot_type=robot.name,
-        use_videos=True,
-        image_writer_threads=4,
+        EEBoundsAndSafety(
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+            max_ee_step_m=0.20,
+        ),
+        GripperVelocityToJoint(speed_factor=20.0),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Build pipeline to convert EE action to joints action
+robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Build pipeline to convert joint observation to EE observation
+robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
+    steps=[
+        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+    ],
+    to_transition=observation_to_transition,
+    to_output=transition_to_observation,
+)
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_REPO_ID,
+    fps=FPS,
+    features=combine_feature_dicts(
+        # Run the feature contract of the pipelines
+        # This tells you how the features would look like after the pipeline steps
+        aggregate_pipeline_dataset_features(
+            pipeline=phone_to_robot_ee_pose_processor,
+            initial_features=create_initial_features(action=phone.action_features),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_joints_to_ee_pose,
+            initial_features=create_initial_features(observation=robot.observation_features),
+            use_videos=True,
+        ),
+    ),
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
+
+# Connect the robot and teleoperator
+robot.connect()
+phone.connect()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="phone_so100_record")
+
+if not robot.is_connected or not phone.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
+
+
+print("Starting record loop. Move your phone to teleoperate the robot...")
+episode_idx = 0
+while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+    log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        teleop=phone,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=phone_to_robot_ee_pose_processor,
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose,
     )
 
-    # Connect the robot and teleoperator
-    robot.connect()
-    phone.connect()
-
-    # Initialize the keyboard listener and rerun visualization
-    listener, events = init_keyboard_listener()
-    init_rerun(session_name="phone_so100_record")
-
-    if not robot.is_connected or not phone.is_connected:
-        raise ValueError("Robot or teleop is not connected!")
-
-    print("Starting record loop. Move your phone to teleoperate the robot...")
-    episode_idx = 0
-    while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-        log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
-
-        # Main record loop
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
+        log_say("Reset the environment")
         record_loop(
             robot=robot,
             events=events,
             fps=FPS,
             teleop=phone,
-            dataset=dataset,
-            control_time_s=EPISODE_TIME_SEC,
+            control_time_s=RESET_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
             teleop_action_processor=phone_to_robot_ee_pose_processor,
@@ -173,42 +184,22 @@ def main():
             robot_observation_processor=robot_joints_to_ee_pose,
         )
 
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
-            log_say("Reset the environment")
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                teleop=phone,
-                control_time_s=RESET_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=phone_to_robot_ee_pose_processor,
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose,
-            )
+    if events["rerecord_episode"]:
+        log_say("Re-recording episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
 
-        if events["rerecord_episode"]:
-            log_say("Re-recording episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+    # Save episode
+    dataset.save_episode()
+    episode_idx += 1
 
-        # Save episode
-        dataset.save_episode()
-        episode_idx += 1
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+phone.disconnect()
+listener.stop()
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    phone.disconnect()
-    listener.stop()
-
-    dataset.finalize()
-    dataset.push_to_hub()
-
-
-if __name__ == "__main__":
-    main()
+dataset.finalize()
+dataset.push_to_hub()

examples/phone_to_so100/replay.py

@@ -29,78 +29,72 @@ from lerobot.robots.so100_follower.robot_kinematic_processor import (
 )
 from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.utils.constants import ACTION
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.utils import log_say
 
 EPISODE_IDX = 0
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+# Initialize the robot config
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
+)
 
-def main():
-    # Initialize the robot config
-    robot_config = SO100FollowerConfig(
-        port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
-    )
+# Initialize the robot
+robot = SO100Follower(robot_config)
 
-    # Initialize the robot
-    robot = SO100Follower(robot_config)
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
-    )
+# Build pipeline to convert EE action to joints action
+robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=False,  # Because replay is open loop
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
 
-    # Build pipeline to convert EE action to joints action
-    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=False,  # Because replay is open loop
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
+# Fetch the dataset to replay
+dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX])
+# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
+episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
+actions = episode_frames.select_columns(ACTION)
 
-    # Fetch the dataset to replay
-    dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX])
-    # Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
-    episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
-    actions = episode_frames.select_columns(ACTION)
+# Connect to the robot
+robot.connect()
 
-    # Connect to the robot
-    robot.connect()
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+print("Starting replay loop...")
+log_say(f"Replaying episode {EPISODE_IDX}")
+for idx in range(len(episode_frames)):
+    t0 = time.perf_counter()
 
-    print("Starting replay loop...")
-    log_say(f"Replaying episode {EPISODE_IDX}")
-    for idx in range(len(episode_frames)):
-        t0 = time.perf_counter()
+    # Get recorded action from dataset
+    ee_action = {
+        name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+    }
 
-        # Get recorded action from dataset
-        ee_action = {
-            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-        }
+    # Get robot observation
+    robot_obs = robot.get_observation()
 
-        # Get robot observation
-        robot_obs = robot.get_observation()
+    # Dataset EE -> robot joints
+    joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
 
-        # Dataset EE -> robot joints
-        joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+    # Send action to robot
+    _ = robot.send_action(joint_action)
 
-        # Send action to robot
-        _ = robot.send_action(joint_action)
+    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
 
-        precise_sleep(1.0 / dataset.fps - (time.perf_counter() - t0))
-
-    # Clean up
-    robot.disconnect()
-
-
-if __name__ == "__main__":
-    main()
+# Clean up
+robot.disconnect()

examples/phone_to_so100/teleoperate.py

@@ -32,90 +32,82 @@ from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
 from lerobot.teleoperators.phone.teleop_phone import Phone
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 FPS = 30
 
+# Initialize the robot and teleoperator
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
+)
+teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
 
-def main():
-    # Initialize the robot and teleoperator
-    robot_config = SO100FollowerConfig(
-        port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
-    )
-    teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
+# Initialize the robot and teleoperator
+robot = SO100Follower(robot_config)
+teleop_device = Phone(teleop_config)
 
-    # Initialize the robot and teleoperator
-    robot = SO100Follower(robot_config)
-    teleop_device = Phone(teleop_config)
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
-    )
+# Build pipeline to convert phone action to ee pose action to joint action
+phone_to_robot_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
+        EEReferenceAndDelta(
+            kinematics=kinematics_solver,
+            end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+            motor_names=list(robot.bus.motors.keys()),
+            use_latched_reference=True,
+        ),
+        EEBoundsAndSafety(
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+            max_ee_step_m=0.10,
+        ),
+        GripperVelocityToJoint(
+            speed_factor=20.0,
+        ),
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
 
-    # Build pipeline to convert phone action to ee pose action to joint action
-    phone_to_robot_joints_processor = RobotProcessorPipeline[
-        tuple[RobotAction, RobotObservation], RobotAction
-    ](
-        steps=[
-            MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
-            EEReferenceAndDelta(
-                kinematics=kinematics_solver,
-                end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
-                motor_names=list(robot.bus.motors.keys()),
-                use_latched_reference=True,
-            ),
-            EEBoundsAndSafety(
-                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
-                max_ee_step_m=0.10,
-            ),
-            GripperVelocityToJoint(
-                speed_factor=20.0,
-            ),
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
+# Connect to the robot and teleoperator
+robot.connect()
+teleop_device.connect()
 
-    # Connect to the robot and teleoperator
-    robot.connect()
-    teleop_device.connect()
+# Init rerun viewer
+init_rerun(session_name="phone_so100_teleop")
 
-    # Init rerun viewer
-    init_rerun(session_name="phone_so100_teleop")
+if not robot.is_connected or not teleop_device.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
 
-    if not robot.is_connected or not teleop_device.is_connected:
-        raise ValueError("Robot or teleop is not connected!")
+print("Starting teleop loop. Move your phone to teleoperate the robot...")
+while True:
+    t0 = time.perf_counter()
 
-    print("Starting teleop loop. Move your phone to teleoperate the robot...")
-    while True:
-        t0 = time.perf_counter()
+    # Get robot observation
+    robot_obs = robot.get_observation()
 
-        # Get robot observation
-        robot_obs = robot.get_observation()
+    # Get teleop action
+    phone_obs = teleop_device.get_action()
 
-        # Get teleop action
-        phone_obs = teleop_device.get_action()
+    # Phone -> EE pose -> Joints transition
+    joint_action = phone_to_robot_joints_processor((phone_obs, robot_obs))
 
-        # Phone -> EE pose -> Joints transition
-        joint_action = phone_to_robot_joints_processor((phone_obs, robot_obs))
+    # Send action to robot
+    _ = robot.send_action(joint_action)
 
-        # Send action to robot
-        _ = robot.send_action(joint_action)
+    # Visualize
+    log_rerun_data(observation=phone_obs, action=joint_action)
 
-        # Visualize
-        log_rerun_data(observation=phone_obs, action=joint_action)
-
-        precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
-
-
-if __name__ == "__main__":
-    main()
+    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))

examples/so100_to_so100_EE/evaluate.py

@@ -52,114 +52,126 @@ TASK_DESCRIPTION = "My task description"
 HF_MODEL_ID = "<hf_username>/<model_repo_id>"
 HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
+# Create the robot configuration & robot
+camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411",
+    id="my_awesome_follower_arm",
+    cameras=camera_config,
+    use_degrees=True,
+)
 
-def main():
-    # Create the robot configuration & robot
-    camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
-    robot_config = SO100FollowerConfig(
-        port="/dev/tty.usbmodem5A460814411",
-        id="my_awesome_follower_arm",
-        cameras=camera_config,
-        use_degrees=True,
-    )
+robot = SO100Follower(robot_config)
 
-    robot = SO100Follower(robot_config)
+# Create policy
+policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
 
-    # Create policy
-    policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
-    )
-
-    # Build pipeline to convert EE action to joints action
-    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Build pipeline to convert joints observation to EE observation
-    robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
-            )
-        ],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-
-    # Create the dataset
-    dataset = LeRobotDataset.create(
-        repo_id=HF_DATASET_ID,
-        fps=FPS,
-        features=combine_feature_dicts(
-            aggregate_pipeline_dataset_features(
-                pipeline=robot_joints_to_ee_pose_processor,
-                initial_features=create_initial_features(observation=robot.observation_features),
-                use_videos=True,
-            ),
-            # User for now should be explicit on the feature keys that were used for record
-            # Alternatively, the user can pass the processor step that has the right features
-            aggregate_pipeline_dataset_features(
-                pipeline=make_default_teleop_action_processor(),
-                initial_features=create_initial_features(
-                    action={
-                        f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
-                        for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
-                    }
-                ),
-                use_videos=True,
-            ),
+# Build pipeline to convert EE action to joints action
+robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=True,
         ),
-        robot_type=robot.name,
-        use_videos=True,
-        image_writer_threads=4,
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Build pipeline to convert joints observation to EE observation
+robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
+    steps=[
+        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+    ],
+    to_transition=observation_to_transition,
+    to_output=transition_to_observation,
+)
+
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_DATASET_ID,
+    fps=FPS,
+    features=combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_joints_to_ee_pose_processor,
+            initial_features=create_initial_features(observation=robot.observation_features),
+            use_videos=True,
+        ),
+        # User for now should be explicit on the feature keys that were used for record
+        # Alternatively, the user can pass the processor step that has the right features
+        aggregate_pipeline_dataset_features(
+            pipeline=make_default_teleop_action_processor(),
+            initial_features=create_initial_features(
+                action={
+                    f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
+                    for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
+                }
+            ),
+            use_videos=True,
+        ),
+    ),
+    robot_type=robot.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
+
+# Build Policy Processors
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=policy,
+    pretrained_path=HF_MODEL_ID,
+    dataset_stats=dataset.meta.stats,
+    # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
+    preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
+)
+
+# Connect the robot and teleoperator
+robot.connect()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="so100_so100_evaluate")
+
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
+
+print("Starting evaluate loop...")
+episode_idx = 0
+for episode_idx in range(NUM_EPISODES):
+    log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
+
+    # Main record loop
+    record_loop(
+        robot=robot,
+        events=events,
+        fps=FPS,
+        policy=policy,
+        preprocessor=preprocessor,  # Pass the pre and post policy processors
+        postprocessor=postprocessor,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=make_default_teleop_action_processor(),
+        robot_action_processor=robot_ee_to_joints_processor,
+        robot_observation_processor=robot_joints_to_ee_pose_processor,
     )
 
-    # Build Policy Processors
-    preprocessor, postprocessor = make_pre_post_processors(
-        policy_cfg=policy,
-        pretrained_path=HF_MODEL_ID,
-        dataset_stats=dataset.meta.stats,
-        # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
-        preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
-    )
-
-    # Connect the robot and teleoperator
-    robot.connect()
-
-    # Initialize the keyboard listener and rerun visualization
-    listener, events = init_keyboard_listener()
-    init_rerun(session_name="so100_so100_evaluate")
-
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
-
-    print("Starting evaluate loop...")
-    episode_idx = 0
-    for episode_idx in range(NUM_EPISODES):
-        log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
-
-        # Main record loop
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
+        log_say("Reset the environment")
         record_loop(
             robot=robot,
             events=events,
             fps=FPS,
-            policy=policy,
-            preprocessor=preprocessor,  # Pass the pre and post policy processors
-            postprocessor=postprocessor,
-            dataset=dataset,
             control_time_s=EPISODE_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
@@ -168,40 +180,21 @@ def main():
             robot_observation_processor=robot_joints_to_ee_pose_processor,
         )
 
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
-            log_say("Reset the environment")
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=FPS,
-                control_time_s=EPISODE_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=make_default_teleop_action_processor(),
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose_processor,
-            )
+    if events["rerecord_episode"]:
+        log_say("Re-record episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
 
-        if events["rerecord_episode"]:
-            log_say("Re-record episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+    # Save episode
+    dataset.save_episode()
+    episode_idx += 1
 
-        # Save episode
-        dataset.save_episode()
-        episode_idx += 1
+# Clean up
+log_say("Stop recording")
+robot.disconnect()
+listener.stop()
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    listener.stop()
-
-    dataset.finalize()
-    dataset.push_to_hub()
-
-
-if __name__ == "__main__":
-    main()
+dataset.finalize()
+dataset.push_to_hub()

examples/so100_to_so100_EE/record.py

@@ -48,122 +48,134 @@ RESET_TIME_SEC = 30
 TASK_DESCRIPTION = "My task description"
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+# Create the robot and teleoperator configurations
+camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+follower_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", cameras=camera_config, use_degrees=True
+)
+leader_config = SO100LeaderConfig(port="/dev/tty.usbmodem5A460819811", id="my_awesome_leader_arm")
 
-def main():
-    # Create the robot and teleoperator configurations
-    camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
-    follower_config = SO100FollowerConfig(
-        port="/dev/tty.usbmodem5A460814411",
-        id="my_awesome_follower_arm",
-        cameras=camera_config,
-        use_degrees=True,
-    )
-    leader_config = SO100LeaderConfig(port="/dev/tty.usbmodem5A460819811", id="my_awesome_leader_arm")
+# Initialize the robot and teleoperator
+follower = SO100Follower(follower_config)
+leader = SO100Leader(leader_config)
 
-    # Initialize the robot and teleoperator
-    follower = SO100Follower(follower_config)
-    leader = SO100Leader(leader_config)
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+follower_kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(follower.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    follower_kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(follower.bus.motors.keys()),
-    )
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+leader_kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(leader.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    leader_kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(leader.bus.motors.keys()),
-    )
-
-    # Build pipeline to convert follower joints to EE observation
-    follower_joints_to_ee = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=follower_kinematics_solver, motor_names=list(follower.bus.motors.keys())
-            ),
-        ],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-
-    # Build pipeline to convert leader joints to EE action
-    leader_joints_to_ee = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys())
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Build pipeline to convert EE action to follower joints
-    ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        [
-            EEBoundsAndSafety(
-                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
-                max_ee_step_m=0.10,
-            ),
-            InverseKinematicsEEToJoints(
-                kinematics=follower_kinematics_solver,
-                motor_names=list(follower.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Create the dataset
-    dataset = LeRobotDataset.create(
-        repo_id=HF_REPO_ID,
-        fps=FPS,
-        features=combine_feature_dicts(
-            # Run the feature contract of the pipelines
-            # This tells you how the features would look like after the pipeline steps
-            aggregate_pipeline_dataset_features(
-                pipeline=leader_joints_to_ee,
-                initial_features=create_initial_features(action=leader.action_features),
-                use_videos=True,
-            ),
-            aggregate_pipeline_dataset_features(
-                pipeline=follower_joints_to_ee,
-                initial_features=create_initial_features(observation=follower.observation_features),
-                use_videos=True,
-            ),
+# Build pipeline to convert follower joints to EE observation
+follower_joints_to_ee = RobotProcessorPipeline[RobotObservation, RobotObservation](
+    steps=[
+        ForwardKinematicsJointsToEE(
+            kinematics=follower_kinematics_solver, motor_names=list(follower.bus.motors.keys())
         ),
-        robot_type=follower.name,
-        use_videos=True,
-        image_writer_threads=4,
+    ],
+    to_transition=observation_to_transition,
+    to_output=transition_to_observation,
+)
+
+# Build pipeline to convert leader joints to EE action
+leader_joints_to_ee = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        ForwardKinematicsJointsToEE(
+            kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys())
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Build pipeline to convert EE action to follower joints
+ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    [
+        EEBoundsAndSafety(
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+            max_ee_step_m=0.10,
+        ),
+        InverseKinematicsEEToJoints(
+            kinematics=follower_kinematics_solver,
+            motor_names=list(follower.bus.motors.keys()),
+            initial_guess_current_joints=True,
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
+
+# Create the dataset
+dataset = LeRobotDataset.create(
+    repo_id=HF_REPO_ID,
+    fps=FPS,
+    features=combine_feature_dicts(
+        # Run the feature contract of the pipelines
+        # This tells you how the features would look like after the pipeline steps
+        aggregate_pipeline_dataset_features(
+            pipeline=leader_joints_to_ee,
+            initial_features=create_initial_features(action=leader.action_features),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=follower_joints_to_ee,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    ),
+    robot_type=follower.name,
+    use_videos=True,
+    image_writer_threads=4,
+)
+
+
+# Connect the robot and teleoperator
+leader.connect()
+follower.connect()
+
+# Initialize the keyboard listener and rerun visualization
+listener, events = init_keyboard_listener()
+init_rerun(session_name="recording_phone")
+
+if not leader.is_connected or not follower.is_connected:
+    raise ValueError("Robot or teleop is not connected!")
+
+print("Starting record loop...")
+episode_idx = 0
+while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+    log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+
+    # Main record loop
+    record_loop(
+        robot=follower,
+        events=events,
+        fps=FPS,
+        teleop=leader,
+        dataset=dataset,
+        control_time_s=EPISODE_TIME_SEC,
+        single_task=TASK_DESCRIPTION,
+        display_data=True,
+        teleop_action_processor=leader_joints_to_ee,
+        robot_action_processor=ee_to_follower_joints,
+        robot_observation_processor=follower_joints_to_ee,
     )
 
-    # Connect the robot and teleoperator
-    leader.connect()
-    follower.connect()
-
-    # Initialize the keyboard listener and rerun visualization
-    listener, events = init_keyboard_listener()
-    init_rerun(session_name="recording_phone")
-
-    if not leader.is_connected or not follower.is_connected:
-        raise ValueError("Robot or teleop is not connected!")
-
-    print("Starting record loop...")
-    episode_idx = 0
-    while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-        log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
-
-        # Main record loop
+    # Reset the environment if not stopping or re-recording
+    if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
+        log_say("Reset the environment")
         record_loop(
             robot=follower,
             events=events,
             fps=FPS,
             teleop=leader,
-            dataset=dataset,
-            control_time_s=EPISODE_TIME_SEC,
+            control_time_s=RESET_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
             teleop_action_processor=leader_joints_to_ee,
@@ -171,42 +183,22 @@ def main():
             robot_observation_processor=follower_joints_to_ee,
         )
 
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
-            log_say("Reset the environment")
-            record_loop(
-                robot=follower,
-                events=events,
-                fps=FPS,
-                teleop=leader,
-                control_time_s=RESET_TIME_SEC,
-                single_task=TASK_DESCRIPTION,
-                display_data=True,
-                teleop_action_processor=leader_joints_to_ee,
-                robot_action_processor=ee_to_follower_joints,
-                robot_observation_processor=follower_joints_to_ee,
-            )
+    if events["rerecord_episode"]:
+        log_say("Re-recording episode")
+        events["rerecord_episode"] = False
+        events["exit_early"] = False
+        dataset.clear_episode_buffer()
+        continue
 
-        if events["rerecord_episode"]:
-            log_say("Re-recording episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+    # Save episode
+    dataset.save_episode()
+    episode_idx += 1
 
-        # Save episode
-        dataset.save_episode()
-        episode_idx += 1
+# Clean up
+log_say("Stop recording")
+leader.disconnect()
+follower.disconnect()
+listener.stop()
 
-    # Clean up
-    log_say("Stop recording")
-    leader.disconnect()
-    follower.disconnect()
-    listener.stop()
-
-    dataset.finalize()
-    dataset.push_to_hub()
-
-
-if __name__ == "__main__":
-    main()
+dataset.finalize()
+dataset.push_to_hub()

examples/so100_to_so100_EE/replay.py

@@ -30,78 +30,72 @@ from lerobot.robots.so100_follower.robot_kinematic_processor import (
 )
 from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.utils.constants import ACTION
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.utils import log_say
 
 EPISODE_IDX = 0
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+# Initialize the robot config
+robot_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
+)
 
-def main():
-    # Initialize the robot config
-    robot_config = SO100FollowerConfig(
-        port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
-    )
+# Initialize the robot
+robot = SO100Follower(robot_config)
 
-    # Initialize the robot
-    robot = SO100Follower(robot_config)
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(robot.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
-    )
+# Build pipeline to convert EE action to joints action
+robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    steps=[
+        InverseKinematicsEEToJoints(
+            kinematics=kinematics_solver,
+            motor_names=list(robot.bus.motors.keys()),
+            initial_guess_current_joints=False,  # Because replay is open loop
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
 
-    # Build pipeline to convert EE action to joints action
-    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=False,  # Because replay is open loop
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
+# Fetch the dataset to replay
+dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX])
+# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
+episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
+actions = episode_frames.select_columns(ACTION)
 
-    # Fetch the dataset to replay
-    dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX])
-    # Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
-    episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
-    actions = episode_frames.select_columns(ACTION)
+# Connect to the robot
+robot.connect()
 
-    # Connect to the robot
-    robot.connect()
+if not robot.is_connected:
+    raise ValueError("Robot is not connected!")
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+print("Starting replay loop...")
+log_say(f"Replaying episode {EPISODE_IDX}")
+for idx in range(len(episode_frames)):
+    t0 = time.perf_counter()
 
-    print("Starting replay loop...")
-    log_say(f"Replaying episode {EPISODE_IDX}")
-    for idx in range(len(episode_frames)):
-        t0 = time.perf_counter()
+    # Get recorded action from dataset
+    ee_action = {
+        name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+    }
 
-        # Get recorded action from dataset
-        ee_action = {
-            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-        }
+    # Get robot observation
+    robot_obs = robot.get_observation()
 
-        # Get robot observation
-        robot_obs = robot.get_observation()
+    # Dataset EE -> robot joints
+    joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
 
-        # Dataset EE -> robot joints
-        joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+    # Send action to robot
+    _ = robot.send_action(joint_action)
 
-        # Send action to robot
-        _ = robot.send_action(joint_action)
+    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
 
-        precise_sleep(1.0 / dataset.fps - (time.perf_counter() - t0))
-
-    # Clean up
-    robot.disconnect()
-
-
-if __name__ == "__main__":
-    main()
+# Clean up
+robot.disconnect()

examples/so100_to_so100_EE/teleoperate.py

@@ -32,96 +32,90 @@ from lerobot.robots.so100_follower.robot_kinematic_processor import (
 from lerobot.robots.so100_follower.so100_follower import SO100Follower
 from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig
 from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 FPS = 30
 
+# Initialize the robot and teleoperator config
+follower_config = SO100FollowerConfig(
+    port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
+)
+leader_config = SO100LeaderConfig(port="/dev/tty.usbmodem5A460819811", id="my_awesome_leader_arm")
 
-def main():
-    # Initialize the robot and teleoperator config
-    follower_config = SO100FollowerConfig(
-        port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
-    )
-    leader_config = SO100LeaderConfig(port="/dev/tty.usbmodem5A460819811", id="my_awesome_leader_arm")
+# Initialize the robot and teleoperator
+follower = SO100Follower(follower_config)
+leader = SO100Leader(leader_config)
 
-    # Initialize the robot and teleoperator
-    follower = SO100Follower(follower_config)
-    leader = SO100Leader(leader_config)
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+follower_kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(follower.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    follower_kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(follower.bus.motors.keys()),
-    )
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+leader_kinematics_solver = RobotKinematics(
+    urdf_path="./SO101/so101_new_calib.urdf",
+    target_frame_name="gripper_frame_link",
+    joint_names=list(leader.bus.motors.keys()),
+)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    leader_kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(leader.bus.motors.keys()),
-    )
+# Build pipeline to convert teleop joints to EE action
+leader_to_ee = RobotProcessorPipeline[RobotAction, RobotAction](
+    steps=[
+        ForwardKinematicsJointsToEE(
+            kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys())
+        ),
+    ],
+    to_transition=robot_action_to_transition,
+    to_output=transition_to_robot_action,
+)
 
-    # Build pipeline to convert teleop joints to EE action
-    leader_to_ee = RobotProcessorPipeline[RobotAction, RobotAction](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys())
-            ),
-        ],
-        to_transition=robot_action_to_transition,
-        to_output=transition_to_robot_action,
-    )
+# build pipeline to convert EE action to robot joints
+ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+    [
+        EEBoundsAndSafety(
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+            max_ee_step_m=0.10,
+        ),
+        InverseKinematicsEEToJoints(
+            kinematics=follower_kinematics_solver,
+            motor_names=list(follower.bus.motors.keys()),
+            initial_guess_current_joints=False,
+        ),
+    ],
+    to_transition=robot_action_observation_to_transition,
+    to_output=transition_to_robot_action,
+)
 
-    # build pipeline to convert EE action to robot joints
-    ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        [
-            EEBoundsAndSafety(
-                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
-                max_ee_step_m=0.10,
-            ),
-            InverseKinematicsEEToJoints(
-                kinematics=follower_kinematics_solver,
-                motor_names=list(follower.bus.motors.keys()),
-                initial_guess_current_joints=False,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
+# Connect to the robot and teleoperator
+follower.connect()
+leader.connect()
 
-    # Connect to the robot and teleoperator
-    follower.connect()
-    leader.connect()
+# Init rerun viewer
+init_rerun(session_name="so100_so100_EE_teleop")
 
-    # Init rerun viewer
-    init_rerun(session_name="so100_so100_EE_teleop")
+print("Starting teleop loop...")
+while True:
+    t0 = time.perf_counter()
 
-    print("Starting teleop loop...")
-    while True:
-        t0 = time.perf_counter()
+    # Get robot observation
+    robot_obs = follower.get_observation()
 
-        # Get robot observation
-        robot_obs = follower.get_observation()
+    # Get teleop observation
+    leader_joints_obs = leader.get_action()
 
-        # Get teleop observation
-        leader_joints_obs = leader.get_action()
+    # teleop joints -> teleop EE action
+    leader_ee_act = leader_to_ee(leader_joints_obs)
 
-        # teleop joints -> teleop EE action
-        leader_ee_act = leader_to_ee(leader_joints_obs)
+    # teleop EE -> robot joints
+    follower_joints_act = ee_to_follower_joints((leader_ee_act, robot_obs))
 
-        # teleop EE -> robot joints
-        follower_joints_act = ee_to_follower_joints((leader_ee_act, robot_obs))
+    # Send action to robot
+    _ = follower.send_action(follower_joints_act)
 
-        # Send action to robot
-        _ = follower.send_action(follower_joints_act)
+    # Visualize
+    log_rerun_data(observation=leader_ee_act, action=follower_joints_act)
 
-        # Visualize
-        log_rerun_data(observation=leader_ee_act, action=follower_joints_act)
-
-        precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
-
-
-if __name__ == "__main__":
-    main()
+    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))

examples/tutorial/act/act_training_example.py

@@ -19,86 +19,80 @@ def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[flo
     return [i / fps for i in delta_indices]
 
 
-def main():
-    output_directory = Path("outputs/robot_learning_tutorial/act")
-    output_directory.mkdir(parents=True, exist_ok=True)
+output_directory = Path("outputs/robot_learning_tutorial/act")
+output_directory.mkdir(parents=True, exist_ok=True)
 
-    # Select your device
-    device = torch.device("mps")  # or "cuda" or "cpu"
+# Select your device
+device = torch.device("mps")  # or "cuda" or "cpu"
 
-    dataset_id = "lerobot/svla_so101_pickplace"
+dataset_id = "lerobot/svla_so101_pickplace"
 
-    # This specifies the inputs the model will be expecting and the outputs it will produce
-    dataset_metadata = LeRobotDatasetMetadata(dataset_id)
-    features = dataset_to_policy_features(dataset_metadata.features)
+# This specifies the inputs the model will be expecting and the outputs it will produce
+dataset_metadata = LeRobotDatasetMetadata(dataset_id)
+features = dataset_to_policy_features(dataset_metadata.features)
 
-    output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
-    input_features = {key: ft for key, ft in features.items() if key not in output_features}
+output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
+input_features = {key: ft for key, ft in features.items() if key not in output_features}
 
-    cfg = ACTConfig(input_features=input_features, output_features=output_features)
-    policy = ACTPolicy(cfg)
-    preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
+cfg = ACTConfig(input_features=input_features, output_features=output_features)
+policy = ACTPolicy(cfg)
+preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
 
-    policy.train()
-    policy.to(device)
+policy.train()
+policy.to(device)
 
-    # To perform action chunking, ACT expects a given number of actions as targets
-    delta_timestamps = {
-        "action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
-    }
+# To perform action chunking, ACT expects a given number of actions as targets
+delta_timestamps = {
+    "action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
+}
 
-    # add image features if they are present
-    delta_timestamps |= {
-        k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps)
-        for k in cfg.image_features
-    }
+# add image features if they are present
+delta_timestamps |= {
+    k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps) for k in cfg.image_features
+}
 
-    # Instantiate the dataset
-    dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
+# Instantiate the dataset
+dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
 
-    # Create the optimizer and dataloader for offline training
-    optimizer = cfg.get_optimizer_preset().build(policy.parameters())
-    batch_size = 32
-    dataloader = torch.utils.data.DataLoader(
-        dataset,
-        batch_size=batch_size,
-        shuffle=True,
-        pin_memory=device.type != "cpu",
-        drop_last=True,
-    )
+# Create the optimizer and dataloader for offline training
+optimizer = cfg.get_optimizer_preset().build(policy.parameters())
+batch_size = 32
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=batch_size,
+    shuffle=True,
+    pin_memory=device.type != "cpu",
+    drop_last=True,
+)
 
-    # Number of training steps and logging frequency
-    training_steps = 1
-    log_freq = 1
+# Number of training steps and logging frequency
+training_steps = 1
+log_freq = 1
 
-    # Run training loop
-    step = 0
-    done = False
-    while not done:
-        for batch in dataloader:
-            batch = preprocessor(batch)
-            loss, _ = policy.forward(batch)
-            loss.backward()
-            optimizer.step()
-            optimizer.zero_grad()
+# Run training loop
+step = 0
+done = False
+while not done:
+    for batch in dataloader:
+        batch = preprocessor(batch)
+        loss, _ = policy.forward(batch)
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
 
-            if step % log_freq == 0:
-                print(f"step: {step} loss: {loss.item():.3f}")
-            step += 1
-            if step >= training_steps:
-                done = True
-                break
+        if step % log_freq == 0:
+            print(f"step: {step} loss: {loss.item():.3f}")
+        step += 1
+        if step >= training_steps:
+            done = True
+            break
 
-    # Save the policy checkpoint, alongside the pre/post processors
-    policy.save_pretrained(output_directory)
-    preprocessor.save_pretrained(output_directory)
-    postprocessor.save_pretrained(output_directory)
+# Save the policy checkpoint, alongside the pre/post processors
+policy.save_pretrained(output_directory)
+preprocessor.save_pretrained(output_directory)
+postprocessor.save_pretrained(output_directory)
 
-    # Save all assets to the Hub
-    policy.push_to_hub("<user>/robot_learning_tutorial_act")
-    preprocessor.push_to_hub("<user>/robot_learning_tutorial_act")
-    postprocessor.push_to_hub("<user>/robot_learning_tutorial_act")
-
-
-if __name__ == "__main__":
-    main()
+# Save all assets to the Hub
+policy.push_to_hub("fracapuano/robot_learning_tutorial_act")
+preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")
+postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")

examples/tutorial/act/act_using_example.py

@@ -8,56 +8,50 @@ from lerobot.policies.utils import build_inference_frame, make_robot_action
 from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
 from lerobot.robots.so100_follower.so100_follower import SO100Follower
 
+device = torch.device("mps")  # or "cuda" or "cpu"
+model_id = "fracapuano/robot_learning_tutorial_act"
+model = ACTPolicy.from_pretrained(model_id)
+
+dataset_id = "lerobot/svla_so101_pickplace"
+# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
+dataset_metadata = LeRobotDatasetMetadata(dataset_id)
+preprocess, postprocess = make_pre_post_processors(model.config, dataset_stats=dataset_metadata.stats)
+
+# # find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+
+# # the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
+
 MAX_EPISODES = 5
 MAX_STEPS_PER_EPISODE = 20
 
+# Robot and environment configuration
+# Camera keys must match the name and resolutions of the ones used for training!
+# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
+camera_config = {
+    "side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+}
 
-def main():
-    device = torch.device("mps")  # or "cuda" or "cpu"
-    model_id = "<user>/robot_learning_tutorial_act"
-    model = ACTPolicy.from_pretrained(model_id)
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+robot = SO100Follower(robot_cfg)
+robot.connect()
 
-    dataset_id = "lerobot/svla_so101_pickplace"
-    # This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
-    dataset_metadata = LeRobotDatasetMetadata(dataset_id)
-    preprocess, postprocess = make_pre_post_processors(model.config, dataset_stats=dataset_metadata.stats)
+for _ in range(MAX_EPISODES):
+    for _ in range(MAX_STEPS_PER_EPISODE):
+        obs = robot.get_observation()
+        obs_frame = build_inference_frame(
+            observation=obs, ds_features=dataset_metadata.features, device=device
+        )
 
-    # # find ports using lerobot-find-port
-    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+        obs = preprocess(obs_frame)
 
-    # # the robot ids are used the load the right calibration files
-    follower_id = ...  # something like "follower_so100"
+        action = model.select_action(obs)
+        action = postprocess(action)
 
-    # Robot and environment configuration
-    # Camera keys must match the name and resolutions of the ones used for training!
-    # You can check the camera keys expected by a model in the info.json card on the model card on the Hub
-    camera_config = {
-        "side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-        "up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-    }
+        action = make_robot_action(action, dataset_metadata.features)
 
-    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-    robot = SO100Follower(robot_cfg)
-    robot.connect()
+        robot.send_action(action)
 
-    for _ in range(MAX_EPISODES):
-        for _ in range(MAX_STEPS_PER_EPISODE):
-            obs = robot.get_observation()
-            obs_frame = build_inference_frame(
-                observation=obs, ds_features=dataset_metadata.features, device=device
-            )
-
-            obs = preprocess(obs_frame)
-
-            action = model.select_action(obs)
-            action = postprocess(action)
-
-            action = make_robot_action(action, dataset_metadata.features)
-
-            robot.send_action(action)
-
-        print("Episode finished! Starting new episode...")
-
-
-if __name__ == "__main__":
-    main()
+    print("Episode finished! Starting new episode...")

examples/tutorial/async-inf/policy_server.py

@@ -1,17 +1,11 @@
 from lerobot.async_inference.configs import PolicyServerConfig
 from lerobot.async_inference.policy_server import serve
 
+host = ...  # something like "127.0.0.1" if you're exposing to localhost
+port = ...  # something like 8080
 
-def main():
-    host = ...  # something like "127.0.0.1" if you're exposing to localhost
-    port = ...  # something like 8080
-
-    config = PolicyServerConfig(
-        host=host,
-        port=port,
-    )
-    serve(config)
-
-
-if __name__ == "__main__":
-    main()
+config = PolicyServerConfig(
+    host=host,
+    port=port,
+)
+serve(config)

examples/tutorial/async-inf/robot_client.py

@@ -6,56 +6,50 @@ from lerobot.async_inference.robot_client import RobotClient
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.robots.so100_follower import SO100FollowerConfig
 
+# these cameras must match the ones expected by the policy - find your cameras with lerobot-find-cameras
+# check the config.json on the Hub for the policy you are using to see the expected camera specs
+camera_cfg = {
+    "up": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "side": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+}
 
-def main():
-    # these cameras must match the ones expected by the policy - find your cameras with lerobot-find-cameras
-    # check the config.json on the Hub for the policy you are using to see the expected camera specs
-    camera_cfg = {
-        "up": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-        "side": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-    }
+# # find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
-    # # find ports using lerobot-find-port
-    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+# # the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
 
-    # # the robot ids are used the load the right calibration files
-    follower_id = ...  # something like "follower_so100"
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_cfg)
 
-    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_cfg)
+server_address = ...  # something like "127.0.0.1:8080" if using localhost
 
-    server_address = ...  # something like "127.0.0.1:8080" if using localhost
+# 3. Create client configuration
+client_cfg = RobotClientConfig(
+    robot=robot_cfg,
+    server_address=server_address,
+    policy_device="mps",
+    policy_type="act",
+    pretrained_name_or_path="fracapuano/robot_learning_tutorial_act",
+    chunk_size_threshold=0.5,  # g
+    actions_per_chunk=50,  # make sure this is less than the max actions of the policy
+)
 
-    # 3. Create client configuration
-    client_cfg = RobotClientConfig(
-        robot=robot_cfg,
-        server_address=server_address,
-        policy_device="mps",
-        policy_type="act",
-        pretrained_name_or_path="<user>/robot_learning_tutorial_act",
-        chunk_size_threshold=0.5,  # g
-        actions_per_chunk=50,  # make sure this is less than the max actions of the policy
-    )
+# 4. Create and start client
+client = RobotClient(client_cfg)
 
-    # 4. Create and start client
-    client = RobotClient(client_cfg)
+# 5. Provide a textual description of the task
+task = ...
 
-    # 5. Provide a textual description of the task
-    task = ...
+if client.start():
+    # Start action receiver thread
+    action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
+    action_receiver_thread.start()
 
-    if client.start():
-        # Start action receiver thread
-        action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
-        action_receiver_thread.start()
-
-        try:
-            # Run the control loop
-            client.control_loop(task)
-        except KeyboardInterrupt:
-            client.stop()
-            action_receiver_thread.join()
-            # (Optionally) plot the action queue size
-            visualize_action_queue_size(client.action_queue_size)
-
-
-if __name__ == "__main__":
-    main()
+    try:
+        # Run the control loop
+        client.control_loop(task)
+    except KeyboardInterrupt:
+        client.stop()
+        action_receiver_thread.join()
+        # (Optionally) plot the action queue size
+        visualize_action_queue_size(client.action_queue_size)

examples/tutorial/diffusion/diffusion_training_example.py

@@ -19,87 +19,81 @@ def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[flo
     return [i / fps for i in delta_indices]
 
 
-def main():
-    output_directory = Path("outputs/robot_learning_tutorial/diffusion")
-    output_directory.mkdir(parents=True, exist_ok=True)
+output_directory = Path("outputs/robot_learning_tutorial/diffusion")
+output_directory.mkdir(parents=True, exist_ok=True)
 
-    # Select your device
-    device = torch.device("mps")  # or "cuda" or "cpu"
+# Select your device
+device = torch.device("mps")  # or "cuda" or "cpu"
 
-    dataset_id = "lerobot/svla_so101_pickplace"
+dataset_id = "lerobot/svla_so101_pickplace"
 
-    # This specifies the inputs the model will be expecting and the outputs it will produce
-    dataset_metadata = LeRobotDatasetMetadata(dataset_id)
-    features = dataset_to_policy_features(dataset_metadata.features)
+# This specifies the inputs the model will be expecting and the outputs it will produce
+dataset_metadata = LeRobotDatasetMetadata(dataset_id)
+features = dataset_to_policy_features(dataset_metadata.features)
 
-    output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
-    input_features = {key: ft for key, ft in features.items() if key not in output_features}
+output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
+input_features = {key: ft for key, ft in features.items() if key not in output_features}
 
-    cfg = DiffusionConfig(input_features=input_features, output_features=output_features)
-    policy = DiffusionPolicy(cfg)
-    preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
+cfg = DiffusionConfig(input_features=input_features, output_features=output_features)
+policy = DiffusionPolicy(cfg)
+preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
 
-    policy.train()
-    policy.to(device)
+policy.train()
+policy.to(device)
 
-    # To perform action chunking, ACT expects a given number of actions as targets
-    delta_timestamps = {
-        "observation.state": make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps),
-        "action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
-    }
+# To perform action chunking, ACT expects a given number of actions as targets
+delta_timestamps = {
+    "observation.state": make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps),
+    "action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
+}
 
-    # add image features if they are present
-    delta_timestamps |= {
-        k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps)
-        for k in cfg.image_features
-    }
+# add image features if they are present
+delta_timestamps |= {
+    k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps) for k in cfg.image_features
+}
 
-    # Instantiate the dataset
-    dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
+# Instantiate the dataset
+dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
 
-    # Create the optimizer and dataloader for offline training
-    optimizer = cfg.get_optimizer_preset().build(policy.parameters())
-    batch_size = 32
-    dataloader = torch.utils.data.DataLoader(
-        dataset,
-        batch_size=batch_size,
-        shuffle=True,
-        pin_memory=device.type != "cpu",
-        drop_last=True,
-    )
+# Create the optimizer and dataloader for offline training
+optimizer = cfg.get_optimizer_preset().build(policy.parameters())
+batch_size = 32
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=batch_size,
+    shuffle=True,
+    pin_memory=device.type != "cpu",
+    drop_last=True,
+)
 
-    # Number of training steps and logging frequency
-    training_steps = 1
-    log_freq = 1
+# Number of training steps and logging frequency
+training_steps = 1
+log_freq = 1
 
-    # Run training loop
-    step = 0
-    done = False
-    while not done:
-        for batch in dataloader:
-            batch = preprocessor(batch)
-            loss, _ = policy.forward(batch)
-            loss.backward()
-            optimizer.step()
-            optimizer.zero_grad()
+# Run training loop
+step = 0
+done = False
+while not done:
+    for batch in dataloader:
+        batch = preprocessor(batch)
+        loss, _ = policy.forward(batch)
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
 
-            if step % log_freq == 0:
-                print(f"step: {step} loss: {loss.item():.3f}")
-            step += 1
-            if step >= training_steps:
-                done = True
-                break
+        if step % log_freq == 0:
+            print(f"step: {step} loss: {loss.item():.3f}")
+        step += 1
+        if step >= training_steps:
+            done = True
+            break
 
-    # Save the policy checkpoint, alongside the pre/post processors
-    policy.save_pretrained(output_directory)
-    preprocessor.save_pretrained(output_directory)
-    postprocessor.save_pretrained(output_directory)
+# Save the policy checkpoint, alongside the pre/post processors
+policy.save_pretrained(output_directory)
+preprocessor.save_pretrained(output_directory)
+postprocessor.save_pretrained(output_directory)
 
-    # Save all assets to the Hub
-    policy.push_to_hub("<user>/robot_learning_tutorial_diffusion")
-    preprocessor.push_to_hub("<user>/robot_learning_tutorial_diffusion")
-    postprocessor.push_to_hub("<user>/robot_learning_tutorial_diffusion")
-
-
-if __name__ == "__main__":
-    main()
+# Save all assets to the Hub
+policy.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
+preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
+postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")

examples/tutorial/diffusion/diffusion_using_example.py

@@ -8,57 +8,53 @@ from lerobot.policies.utils import build_inference_frame, make_robot_action
 from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
 from lerobot.robots.so100_follower.so100_follower import SO100Follower
 
+device = torch.device("mps")  # or "cuda" or "cpu"
+model_id = "fracapuano/robot_learning_tutorial_diffusion"
+
+model = DiffusionPolicy.from_pretrained(model_id)
+
+dataset_id = "lerobot/svla_so101_pickplace"
+# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
+dataset_metadata = LeRobotDatasetMetadata(dataset_id)
+preprocess, postprocess = make_pre_post_processors(
+    model.config, model_id, dataset_stats=dataset_metadata.stats
+)
+
 MAX_EPISODES = 5
 MAX_STEPS_PER_EPISODE = 20
 
 
-def main():
-    device = torch.device("mps")  # or "cuda" or "cpu"
-    model_id = "<user>/robot_learning_tutorial_diffusion"
+# # find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
-    model = DiffusionPolicy.from_pretrained(model_id)
+# # the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
 
-    dataset_id = "lerobot/svla_so101_pickplace"
-    # This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
-    dataset_metadata = LeRobotDatasetMetadata(dataset_id)
-    preprocess, postprocess = make_pre_post_processors(
-        model.config, model_id, dataset_stats=dataset_metadata.stats
-    )
+# Robot and environment configuration
+# Camera keys must match the name and resolutions of the ones used for training!
+# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
+camera_config = {
+    "side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+}
 
-    # # find ports using lerobot-find-port
-    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
-
-    # # the robot ids are used the load the right calibration files
-    follower_id = ...  # something like "follower_so100"
-
-    # Robot and environment configuration
-    # Camera keys must match the name and resolutions of the ones used for training!
-    # You can check the camera keys expected by a model in the info.json card on the model card on the Hub
-    camera_config = {
-        "side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-        "up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-    }
-
-    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-    robot = SO100Follower(robot_cfg)
-    robot.connect()
-
-    for _ in range(MAX_EPISODES):
-        for _ in range(MAX_STEPS_PER_EPISODE):
-            obs = robot.get_observation()
-            obs_frame = build_inference_frame(
-                observation=obs, ds_features=dataset_metadata.features, device=device
-            )
-
-            obs = preprocess(obs_frame)
-
-            action = model.select_action(obs)
-            action = postprocess(action)
-            action = make_robot_action(action, dataset_metadata.features)
-            robot.send_action(action)
-
-        print("Episode finished! Starting new episode...")
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+robot = SO100Follower(robot_cfg)
+robot.connect()
 
 
-if __name__ == "__main__":
-    main()
+for _ in range(MAX_EPISODES):
+    for _ in range(MAX_STEPS_PER_EPISODE):
+        obs = robot.get_observation()
+        obs_frame = build_inference_frame(
+            observation=obs, ds_features=dataset_metadata.features, device=device
+        )
+
+        obs = preprocess(obs_frame)
+
+        action = model.select_action(obs)
+        action = postprocess(action)
+        action = make_robot_action(action, dataset_metadata.features)
+        robot.send_action(action)
+
+    print("Episode finished! Starting new episode...")

examples/tutorial/pi0/using_pi0_example.py

@@ -11,63 +11,57 @@ from lerobot.robots.so100_follower.so100_follower import SO100Follower
 MAX_EPISODES = 5
 MAX_STEPS_PER_EPISODE = 20
 
+device = torch.device("mps")  # or "cuda" or "cpu"
+model_id = "lerobot/pi0_base"
 
-def main():
-    device = torch.device("mps")  # or "cuda" or "cpu"
-    model_id = "lerobot/pi0_base"
+model = PI0Policy.from_pretrained(model_id)
 
-    model = PI0Policy.from_pretrained(model_id)
+preprocess, postprocess = make_pre_post_processors(
+    model.config,
+    model_id,
+    # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
+    preprocessor_overrides={"device_processor": {"device": str(device)}},
+)
 
-    preprocess, postprocess = make_pre_post_processors(
-        model.config,
-        model_id,
-        # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
-        preprocessor_overrides={"device_processor": {"device": str(device)}},
-    )
+# find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
-    # find ports using lerobot-find-port
-    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+# the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
 
-    # the robot ids are used the load the right calibration files
-    follower_id = ...  # something like "follower_so100"
+# Robot and environment configuration
+# Camera keys must match the name and resolutions of the ones used for training!
+# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
+camera_config = {
+    "base_0_rgb": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "left_wrist_0_rgb": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+    "right_wrist_0_rgb": OpenCVCameraConfig(index_or_path=2, width=640, height=480, fps=30),
+}
 
-    # Robot and environment configuration
-    # Camera keys must match the name and resolutions of the ones used for training!
-    # You can check the camera keys expected by a model in the info.json card on the model card on the Hub
-    camera_config = {
-        "base_0_rgb": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-        "left_wrist_0_rgb": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-        "right_wrist_0_rgb": OpenCVCameraConfig(index_or_path=2, width=640, height=480, fps=30),
-    }
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+robot = SO100Follower(robot_cfg)
+robot.connect()
 
-    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-    robot = SO100Follower(robot_cfg)
-    robot.connect()
+task = ""  # something like "pick the red block"
+robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
 
-    task = ""  # something like "pick the red block"
-    robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
+# This is used to match the raw observation keys to the keys expected by the policy
+action_features = hw_to_dataset_features(robot.action_features, "action")
+obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+dataset_features = {**action_features, **obs_features}
 
-    # This is used to match the raw observation keys to the keys expected by the policy
-    action_features = hw_to_dataset_features(robot.action_features, "action")
-    obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-    dataset_features = {**action_features, **obs_features}
+for _ in range(MAX_EPISODES):
+    for _ in range(MAX_STEPS_PER_EPISODE):
+        obs = robot.get_observation()
+        obs_frame = build_inference_frame(
+            observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
+        )
 
-    for _ in range(MAX_EPISODES):
-        for _ in range(MAX_STEPS_PER_EPISODE):
-            obs = robot.get_observation()
-            obs_frame = build_inference_frame(
-                observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
-            )
+        obs = preprocess(obs_frame)
 
-            obs = preprocess(obs_frame)
+        action = model.select_action(obs)
+        action = postprocess(action)
+        action = make_robot_action(action, dataset_features)
+        robot.send_action(action)
 
-            action = model.select_action(obs)
-            action = postprocess(action)
-            action = make_robot_action(action, dataset_features)
-            robot.send_action(action)
-
-        print("Episode finished! Starting new episode...")
-
-
-if __name__ == "__main__":
-    main()
+    print("Episode finished! Starting new episode...")

examples/tutorial/rl/hilserl_example.py

@@ -20,8 +20,6 @@ from lerobot.teleoperators.utils import TeleopEvents
 
 LOG_EVERY = 10
 SEND_EVERY = 10
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
 
 
 def run_learner(
@@ -225,123 +223,123 @@ def make_policy_obs(obs, device: torch.device = "cpu"):
     }
 
 
-def main():
-    """Main function - coordinates actor and learner processes."""
+"""Main function - coordinates actor and learner processes."""
 
-    device = "mps"  # or "cuda" or "cpu"
-    output_directory = Path("outputs/robot_learning_tutorial/hil_serl")
-    output_directory.mkdir(parents=True, exist_ok=True)
+device = "mps"  # or "cuda" or "cpu"
+output_directory = Path("outputs/robot_learning_tutorial/hil_serl")
+output_directory.mkdir(parents=True, exist_ok=True)
 
-    # find ports using lerobot-find-port
-    follower_port = ...
-    leader_port = ...
+# find ports using lerobot-find-port
+follower_port = ...
+leader_port = ...
 
-    # the robot ids are used the load the right calibration files
-    follower_id = ...
-    leader_id = ...
+# the robot ids are used the load the right calibration files
+follower_id = ...
+leader_id = ...
 
-    # A pretrained model (to be used in-distribution!)
-    reward_classifier_id = "<user>/reward_classifier_hil_serl_example"
-    reward_classifier = Classifier.from_pretrained(reward_classifier_id)
+# A pretrained model (to be used in-distribution!)
+reward_classifier_id = "fracapuano/reward_classifier_hil_serl_example"
+reward_classifier = Classifier.from_pretrained(reward_classifier_id)
 
-    reward_classifier.to(device)
-    reward_classifier.eval()
+reward_classifier.to(device)
+reward_classifier.eval()
 
-    # Robot and environment configuration
-    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id)
-    teleop_cfg = SO100LeaderConfig(port=leader_port, id=leader_id)
-    processor_cfg = HILSerlProcessorConfig(control_mode="leader")
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
 
-    env_cfg = HILSerlRobotEnvConfig(robot=robot_cfg, teleop=teleop_cfg, processor=processor_cfg)
+# Robot and environment configuration
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id)
+teleop_cfg = SO100LeaderConfig(port=leader_port, id=leader_id)
+processor_cfg = HILSerlProcessorConfig(control_mode="leader")
 
-    # Create robot environment
-    env, teleop_device = make_robot_env(env_cfg)
+env_cfg = HILSerlRobotEnvConfig(robot=robot_cfg, teleop=teleop_cfg, processor=processor_cfg)
 
-    obs_features = hw_to_dataset_features(env.robot.observation_features, "observation")
-    action_features = hw_to_dataset_features(env.robot.action_features, "action")
+# Create robot environment
+env, teleop_device = make_robot_env(env_cfg)
 
-    # Create SAC policy for action selection
-    policy_cfg = SACConfig(
-        device=device,
-        input_features=obs_features,
-        output_features=action_features,
-    )
+obs_features = hw_to_dataset_features(env.robot.observation_features, "observation")
+action_features = hw_to_dataset_features(env.robot.action_features, "action")
 
-    policy_actor = SACPolicy(policy_cfg)
-    policy_learner = SACPolicy(policy_cfg)
+# Create SAC policy for action selection
+policy_cfg = SACConfig(
+    device=device,
+    input_features=obs_features,
+    output_features=action_features,
+)
 
-    demonstrations_repo_id = "lerobot/example_hil_serl_dataset"
-    offline_dataset = LeRobotDataset(repo_id=demonstrations_repo_id)
+policy_actor = SACPolicy(policy_cfg)
+policy_learner = SACPolicy(policy_cfg)
 
-    # Online buffer: initialized from scratch
-    online_replay_buffer = ReplayBuffer(device=device, state_keys=list(obs_features.keys()))
-    # Offline buffer: Created from dataset (pre-populated it with demonstrations)
-    offline_replay_buffer = ReplayBuffer.from_lerobot_dataset(
-        lerobot_dataset=offline_dataset, device=device, state_keys=list(obs_features.keys())
-    )
+demonstrations_repo_id = "lerobot/example_hil_serl_dataset"
+offline_dataset = LeRobotDataset(repo_id=demonstrations_repo_id)
 
-    # Create communication channels between learner and actor processes
-    transitions_queue = mp.Queue(maxsize=10)
-    parameters_queue = mp.Queue(maxsize=2)
-    shutdown_event = mp.Event()
+# Online buffer: initialized from scratch
+online_replay_buffer = ReplayBuffer(device=device, state_keys=list(obs_features.keys()))
+# Offline buffer: Created from dataset (pre-populated it with demonstrations)
+offline_replay_buffer = ReplayBuffer.from_lerobot_dataset(
+    lerobot_dataset=offline_dataset, device=device, state_keys=list(obs_features.keys())
+)
 
-    # Signal handler for graceful shutdown
-    def signal_handler(sig):
-        print(f"\nSignal {sig} received, shutting down...")
-        shutdown_event.set()
-
-    signal.signal(signal.SIGINT, signal_handler)
-    signal.signal(signal.SIGTERM, signal_handler)
-
-    # Create processes
-    learner_process = mp.Process(
-        target=run_learner,
-        args=(
-            transitions_queue,
-            parameters_queue,
-            shutdown_event,
-            policy_learner,
-            online_replay_buffer,
-            offline_replay_buffer,
-        ),
-        kwargs={"device": device},  # can run on accelerated hardware for training
-    )
-
-    actor_process = mp.Process(
-        target=run_actor,
-        args=(
-            transitions_queue,
-            parameters_queue,
-            shutdown_event,
-            policy_actor,
-            reward_classifier,
-            env_cfg,
-            output_directory,
-        ),
-        kwargs={"device": "cpu"},  # actor is frozen, can run on CPU or accelerate for inference
-    )
-
-    learner_process.start()
-    actor_process.start()
-
-    try:
-        # Wait for actor to finish (it controls the episode loop)
-        actor_process.join()
-        shutdown_event.set()
-        learner_process.join(timeout=10)
-
-    except KeyboardInterrupt:
-        print("Main process interrupted")
-        shutdown_event.set()
-        actor_process.join(timeout=5)
-        learner_process.join(timeout=10)
-
-    finally:
-        if learner_process.is_alive():
-            learner_process.terminate()
-        if actor_process.is_alive():
-            actor_process.terminate()
+# Create communication channels between learner and actor processes
+transitions_queue = mp.Queue(maxsize=10)
+parameters_queue = mp.Queue(maxsize=2)
+shutdown_event = mp.Event()
 
 
-if __name__ == "__main__":
-    main()
+# Signal handler for graceful shutdown
+def signal_handler(sig):
+    print(f"\nSignal {sig} received, shutting down...")
+    shutdown_event.set()
+
+
+signal.signal(signal.SIGINT, signal_handler)
+signal.signal(signal.SIGTERM, signal_handler)
+
+# Create processes
+learner_process = mp.Process(
+    target=run_learner,
+    args=(
+        transitions_queue,
+        parameters_queue,
+        shutdown_event,
+        policy_learner,
+        online_replay_buffer,
+        offline_replay_buffer,
+    ),
+    kwargs={"device": device},  # can run on accelerated hardware for training
+)
+
+actor_process = mp.Process(
+    target=run_actor,
+    args=(
+        transitions_queue,
+        parameters_queue,
+        shutdown_event,
+        policy_actor,
+        reward_classifier,
+        env_cfg,
+        output_directory,
+    ),
+    kwargs={"device": "cpu"},  # actor is frozen, can run on CPU or accelerate for inference
+)
+
+learner_process.start()
+actor_process.start()
+
+try:
+    # Wait for actor to finish (it controls the episode loop)
+    actor_process.join()
+    shutdown_event.set()
+    learner_process.join(timeout=10)
+
+except KeyboardInterrupt:
+    print("Main process interrupted")
+    shutdown_event.set()
+    actor_process.join(timeout=5)
+    learner_process.join(timeout=10)
+
+finally:
+    if learner_process.is_alive():
+        learner_process.terminate()
+    if actor_process.is_alive():
+        actor_process.terminate()

examples/tutorial/rl/reward_classifier_example.py

@@ -4,64 +4,59 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.policies.factory import make_policy, make_pre_post_processors
 from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
 
+# Device to use for training
+device = "mps"  # or "cuda", or "cpu"
 
-def main():
-    # Device to use for training
-    device = "mps"  # or "cuda", or "cpu"
+# Load the dataset used for training
+repo_id = "lerobot/example_hil_serl_dataset"
+dataset = LeRobotDataset(repo_id)
 
-    # Load the dataset used for training
-    repo_id = "lerobot/example_hil_serl_dataset"
-    dataset = LeRobotDataset(repo_id)
+# Configure the policy to extract features from the image frames
+camera_keys = dataset.meta.camera_keys
 
-    # Configure the policy to extract features from the image frames
-    camera_keys = dataset.meta.camera_keys
+config = RewardClassifierConfig(
+    num_cameras=len(camera_keys),
+    device=device,
+    # backbone model to extract features from the image frames
+    model_name="microsoft/resnet-18",
+)
 
-    config = RewardClassifierConfig(
-        num_cameras=len(camera_keys),
-        device=device,
-        # backbone model to extract features from the image frames
-        model_name="microsoft/resnet-18",
-    )
-
-    # Make policy, preprocessor, and optimizer
-    policy = make_policy(config, ds_meta=dataset.meta)
-    optimizer = config.get_optimizer_preset().build(policy.parameters())
-    preprocessor, _ = make_pre_post_processors(policy_cfg=config, dataset_stats=dataset.meta.stats)
-
-    classifier_id = "<user>/reward_classifier_hil_serl_example"
-
-    # Instantiate a dataloader
-    dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
-
-    # Training loop
-    num_epochs = 5
-    for epoch in range(num_epochs):
-        total_loss = 0
-        total_accuracy = 0
-        for batch in dataloader:
-            # Preprocess the batch and move it to the correct device.
-            batch = preprocessor(batch)
-
-            # Forward pass
-            loss, output_dict = policy.forward(batch)
-
-            # Backward pass and optimization
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-            total_loss += loss.item()
-            total_accuracy += output_dict["accuracy"]
-
-        avg_loss = total_loss / len(dataloader)
-        avg_accuracy = total_accuracy / len(dataloader)
-        print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {avg_loss:.4f}, Accuracy: {avg_accuracy:.2f}%")
-
-    print("Training finished!")
-
-    # You can now save the trained policy.
-    policy.push_to_hub(classifier_id)
+# Make policy, preprocessor, and optimizer
+policy = make_policy(config, ds_meta=dataset.meta)
+optimizer = config.get_optimizer_preset().build(policy.parameters())
+preprocessor, _ = make_pre_post_processors(policy_cfg=config, dataset_stats=dataset.meta.stats)
 
 
-if __name__ == "__main__":
-    main()
+classifier_id = "fracapuano/reward_classifier_hil_serl_example"
+
+# Instantiate a dataloader
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
+
+# Training loop
+num_epochs = 5
+for epoch in range(num_epochs):
+    total_loss = 0
+    total_accuracy = 0
+    for batch in dataloader:
+        # Preprocess the batch and move it to the correct device.
+        batch = preprocessor(batch)
+
+        # Forward pass
+        loss, output_dict = policy.forward(batch)
+
+        # Backward pass and optimization
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        total_loss += loss.item()
+        total_accuracy += output_dict["accuracy"]
+
+    avg_loss = total_loss / len(dataloader)
+    avg_accuracy = total_accuracy / len(dataloader)
+    print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {avg_loss:.4f}, Accuracy: {avg_accuracy:.2f}%")
+
+print("Training finished!")
+
+# You can now save the trained policy.
+policy.push_to_hub(classifier_id)

examples/tutorial/smolvla/using_smolvla_example.py

@@ -11,62 +11,56 @@ from lerobot.robots.so100_follower.so100_follower import SO100Follower
 MAX_EPISODES = 5
 MAX_STEPS_PER_EPISODE = 20
 
+device = torch.device("mps")  # or "cuda" or "cpu"
+model_id = "lerobot/smolvla_base"
 
-def main():
-    device = torch.device("mps")  # or "cuda" or "cpu"
-    model_id = "lerobot/smolvla_base"
+model = SmolVLAPolicy.from_pretrained(model_id)
 
-    model = SmolVLAPolicy.from_pretrained(model_id)
+preprocess, postprocess = make_pre_post_processors(
+    model.config,
+    model_id,
+    # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
+    preprocessor_overrides={"device_processor": {"device": str(device)}},
+)
 
-    preprocess, postprocess = make_pre_post_processors(
-        model.config,
-        model_id,
-        # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
-        preprocessor_overrides={"device_processor": {"device": str(device)}},
-    )
+# find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
-    # find ports using lerobot-find-port
-    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+# the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
 
-    # the robot ids are used the load the right calibration files
-    follower_id = ...  # something like "follower_so100"
+# Robot and environment configuration
+# Camera keys must match the name and resolutions of the ones used for training!
+# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
+camera_config = {
+    "camera1": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "camera2": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+}
 
-    # Robot and environment configuration
-    # Camera keys must match the name and resolutions of the ones used for training!
-    # You can check the camera keys expected by a model in the info.json card on the model card on the Hub
-    camera_config = {
-        "camera1": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
-        "camera2": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
-    }
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+robot = SO100Follower(robot_cfg)
+robot.connect()
 
-    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-    robot = SO100Follower(robot_cfg)
-    robot.connect()
+task = ""  # something like "pick the red block"
+robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
 
-    task = ""  # something like "pick the red block"
-    robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
+# This is used to match the raw observation keys to the keys expected by the policy
+action_features = hw_to_dataset_features(robot.action_features, "action")
+obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+dataset_features = {**action_features, **obs_features}
 
-    # This is used to match the raw observation keys to the keys expected by the policy
-    action_features = hw_to_dataset_features(robot.action_features, "action")
-    obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-    dataset_features = {**action_features, **obs_features}
+for _ in range(MAX_EPISODES):
+    for _ in range(MAX_STEPS_PER_EPISODE):
+        obs = robot.get_observation()
+        obs_frame = build_inference_frame(
+            observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
+        )
 
-    for _ in range(MAX_EPISODES):
-        for _ in range(MAX_STEPS_PER_EPISODE):
-            obs = robot.get_observation()
-            obs_frame = build_inference_frame(
-                observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
-            )
+        obs = preprocess(obs_frame)
 
-            obs = preprocess(obs_frame)
+        action = model.select_action(obs)
+        action = postprocess(action)
+        action = make_robot_action(action, dataset_features)
+        robot.send_action(action)
 
-            action = model.select_action(obs)
-            action = postprocess(action)
-            action = make_robot_action(action, dataset_features)
-            robot.send_action(action)
-
-        print("Episode finished! Starting new episode...")
-
-
-if __name__ == "__main__":
-    main()
+    print("Episode finished! Starting new episode...")

examples/unitree_g1/gr00t_locomotion.py

@@ -1,347 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Example: GR00T Locomotion with Pre-loaded Policies
-
-This example demonstrates the NEW pattern for loading GR00T policies externally
-and passing them to the robot class.
-"""
-
-import argparse
-import logging
-import threading
-import time
-from collections import deque
-
-import numpy as np
-import onnxruntime as ort
-from huggingface_hub import hf_hub_download
-
-from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
-from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
-
-logger = logging.getLogger(__name__)
-
-GROOT_DEFAULT_ANGLES = np.zeros(29, dtype=np.float32)
-GROOT_DEFAULT_ANGLES[[0, 6]] = -0.1  # hip pitch
-GROOT_DEFAULT_ANGLES[[3, 9]] = 0.3  # knee
-GROOT_DEFAULT_ANGLES[[4, 10]] = -0.2  # ankle pitch
-
-MISSING_JOINTS = []
-G1_MODEL = "g1_23"  # or "g1_29"
-if G1_MODEL == "g1_23":
-    MISSING_JOINTS = [12, 14, 20, 21, 27, 28]  # waist yaw/pitch, wrist pitch/yaw
-
-LOCOMOTION_ACTION_SCALE = 0.25
-
-LOCOMOTION_CONTROL_DT = 0.02
-
-ANG_VEL_SCALE: float = 0.25
-DOF_POS_SCALE: float = 1.0
-DOF_VEL_SCALE: float = 0.05
-CMD_SCALE: list = [2.0, 2.0, 0.25]
-
-
-DEFAULT_GROOT_REPO_ID = "nepyope/GR00T-WholeBodyControl_g1"
-
-
-def load_groot_policies(
-    repo_id: str = DEFAULT_GROOT_REPO_ID,
-) -> tuple[ort.InferenceSession, ort.InferenceSession]:
-    """Load GR00T dual-policy system (Balance + Walk) from Hugging Face Hub.
-
-    Args:
-        repo_id: Hugging Face Hub repository ID containing the ONNX policies.
-    """
-    logger.info(f"Loading GR00T dual-policy system from Hugging Face Hub ({repo_id})...")
-
-    # Download ONNX policies from Hugging Face Hub
-    balance_path = hf_hub_download(
-        repo_id=repo_id,
-        filename="GR00T-WholeBodyControl-Balance.onnx",
-    )
-    walk_path = hf_hub_download(
-        repo_id=repo_id,
-        filename="GR00T-WholeBodyControl-Walk.onnx",
-    )
-
-    # Load ONNX policies
-    policy_balance = ort.InferenceSession(balance_path)
-    policy_walk = ort.InferenceSession(walk_path)
-
-    logger.info("GR00T policies loaded successfully")
-
-    return policy_balance, policy_walk
-
-
-class GrootLocomotionController:
-    """
-    Handles GR00T-style locomotion control for the Unitree G1 robot.
-
-    This controller manages:
-    - Dual-policy system (Balance + Walk)
-    - 29-joint observation processing
-    - 15D action output (legs + waist)
-    - Policy inference and motor command generation
-    """
-
-    def __init__(self, policy_balance, policy_walk, robot, config):
-        self.policy_balance = policy_balance
-        self.policy_walk = policy_walk
-        self.robot = robot
-        self.config = config
-
-        self.locomotion_cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32)  # vx, vy, theta_dot
-
-        # GR00T-specific state
-        self.groot_qj_all = np.zeros(29, dtype=np.float32)
-        self.groot_dqj_all = np.zeros(29, dtype=np.float32)
-        self.groot_action = np.zeros(15, dtype=np.float32)
-        self.groot_obs_single = np.zeros(86, dtype=np.float32)
-        self.groot_obs_history = deque(maxlen=6)
-        self.groot_obs_stacked = np.zeros(516, dtype=np.float32)
-        self.groot_height_cmd = 0.74  # Default base height
-        self.groot_orientation_cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32)
-
-        # input to gr00t is 6 frames (6*86D=516)
-        for _ in range(6):
-            self.groot_obs_history.append(np.zeros(86, dtype=np.float32))
-
-        # Thread management
-        self.locomotion_running = False
-        self.locomotion_thread = None
-
-        logger.info("GrootLocomotionController initialized")
-
-    def groot_locomotion_run(self):
-        # get current observation
-        robot_state = self.robot.get_observation()
-
-        if robot_state is None:
-            return
-
-        # get command from remote controller
-        if robot_state.wireless_remote is not None:
-            self.robot.remote_controller.set(robot_state.wireless_remote)
-            if self.robot.remote_controller.button[0]:  # R1 - raise waist
-                self.groot_height_cmd += 0.001
-                self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
-            if self.robot.remote_controller.button[4]:  # R2 - lower waist
-                self.groot_height_cmd -= 0.001
-                self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
-        else:
-            self.robot.remote_controller.lx = 0.0
-            self.robot.remote_controller.ly = 0.0
-            self.robot.remote_controller.rx = 0.0
-            self.robot.remote_controller.ry = 0.0
-
-        self.locomotion_cmd[0] = self.robot.remote_controller.ly  # forward/backward
-        self.locomotion_cmd[1] = self.robot.remote_controller.lx * -1  # left/right
-        self.locomotion_cmd[2] = self.robot.remote_controller.rx * -1  # rotation rate
-
-        for i in range(29):
-            self.groot_qj_all[i] = robot_state.motor_state[i].q
-            self.groot_dqj_all[i] = robot_state.motor_state[i].dq
-
-        # adapt observation for g1_23dof
-        for idx in MISSING_JOINTS:
-            self.groot_qj_all[idx] = 0.0
-            self.groot_dqj_all[idx] = 0.0
-
-        # Scale joint positions and velocities
-        qj_obs = self.groot_qj_all.copy()
-        dqj_obs = self.groot_dqj_all.copy()
-
-        # express imu data in gravity frame of reference
-        quat = robot_state.imu_state.quaternion
-        ang_vel = np.array(robot_state.imu_state.gyroscope, dtype=np.float32)
-        gravity_orientation = self.robot.get_gravity_orientation(quat)
-
-        # scale joint positions and velocities before policy inference
-        qj_obs = (qj_obs - GROOT_DEFAULT_ANGLES) * DOF_POS_SCALE
-        dqj_obs = dqj_obs * DOF_VEL_SCALE
-        ang_vel_scaled = ang_vel * ANG_VEL_SCALE
-
-        # build single frame observation
-        self.groot_obs_single[:3] = self.locomotion_cmd * np.array(CMD_SCALE)
-        self.groot_obs_single[3] = self.groot_height_cmd
-        self.groot_obs_single[4:7] = self.groot_orientation_cmd
-        self.groot_obs_single[7:10] = ang_vel_scaled
-        self.groot_obs_single[10:13] = gravity_orientation
-        self.groot_obs_single[13:42] = qj_obs
-        self.groot_obs_single[42:71] = dqj_obs
-        self.groot_obs_single[71:86] = self.groot_action  # 15D previous actions
-
-        # Add to history and stack observations (6 frames × 86D = 516D)
-        self.groot_obs_history.append(self.groot_obs_single.copy())
-
-        # Stack all 6 frames into 516D vector
-        for i, obs_frame in enumerate(self.groot_obs_history):
-            start_idx = i * 86
-            end_idx = start_idx + 86
-            self.groot_obs_stacked[start_idx:end_idx] = obs_frame
-
-        # Run policy inference (ONNX) with 516D stacked observation
-
-        cmd_magnitude = np.linalg.norm(self.locomotion_cmd)
-
-        selected_policy = (
-            self.policy_balance if cmd_magnitude < 0.05 else self.policy_walk
-        )  # balance/standing policy for small commands, walking policy for movement commands
-
-        # run policy inference
-        ort_inputs = {selected_policy.get_inputs()[0].name: np.expand_dims(self.groot_obs_stacked, axis=0)}
-        ort_outs = selected_policy.run(None, ort_inputs)
-        self.groot_action = ort_outs[0].squeeze()
-
-        # transform action back to target joint positions
-        target_dof_pos_15 = GROOT_DEFAULT_ANGLES[:15] + self.groot_action * LOCOMOTION_ACTION_SCALE
-
-        # command motors
-        for i in range(15):
-            motor_idx = i
-            self.robot.msg.motor_cmd[motor_idx].q = target_dof_pos_15[i]
-            self.robot.msg.motor_cmd[motor_idx].qd = 0
-            self.robot.msg.motor_cmd[motor_idx].kp = self.robot.kp[motor_idx]
-            self.robot.msg.motor_cmd[motor_idx].kd = self.robot.kd[motor_idx]
-            self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-        # adapt action for g1_23dof
-        for joint_idx in MISSING_JOINTS:
-            self.robot.msg.motor_cmd[joint_idx].q = 0.0
-            self.robot.msg.motor_cmd[joint_idx].qd = 0
-            self.robot.msg.motor_cmd[joint_idx].kp = self.robot.kp[joint_idx]
-            self.robot.msg.motor_cmd[joint_idx].kd = self.robot.kd[joint_idx]
-            self.robot.msg.motor_cmd[joint_idx].tau = 0
-
-        # send action to robot
-        self.robot.send_action(self.robot.msg)
-
-    def _locomotion_thread_loop(self):
-        """Background thread that runs the locomotion policy at specified rate."""
-        logger.info("Locomotion thread started")
-        while self.locomotion_running:
-            start_time = time.time()
-            try:
-                self.groot_locomotion_run()
-            except Exception as e:
-                logger.error(f"Error in locomotion loop: {e}")
-
-            # Sleep to maintain control rate
-            elapsed = time.time() - start_time
-            sleep_time = max(0, LOCOMOTION_CONTROL_DT - elapsed)
-            time.sleep(sleep_time)
-        logger.info("Locomotion thread stopped")
-
-    def start_locomotion_thread(self):
-        if self.locomotion_running:
-            logger.warning("Locomotion thread already running")
-            return
-
-        logger.info("Starting locomotion control thread...")
-        self.locomotion_running = True
-        self.locomotion_thread = threading.Thread(target=self._locomotion_thread_loop, daemon=True)
-        self.locomotion_thread.start()
-
-        logger.info("Locomotion control thread started!")
-
-    def stop_locomotion_thread(self):
-        if not self.locomotion_running:
-            return
-
-        logger.info("Stopping locomotion control thread...")
-        self.locomotion_running = False
-        if self.locomotion_thread:
-            self.locomotion_thread.join(timeout=2.0)
-        logger.info("Locomotion control thread stopped")
-
-    def reset_robot(self):
-        """Move robot legs to default standing position over 2 seconds (arms are not moved)."""
-        total_time = 3.0
-        num_step = int(total_time / self.robot.control_dt)
-
-        # Only control legs, not arms (first 12 joints)
-        default_pos = GROOT_DEFAULT_ANGLES  # First 12 values are leg angles
-        dof_size = len(default_pos)
-
-        # Get current lowstate
-        robot_state = self.robot.get_observation()
-
-        # Record the current leg positions
-        init_dof_pos = np.zeros(dof_size, dtype=np.float32)
-        for i in range(dof_size):
-            init_dof_pos[i] = robot_state.motor_state[i].q
-
-        # Move legs to default pos
-        for i in range(num_step):
-            alpha = i / num_step
-            for motor_idx in range(dof_size):
-                target_pos = default_pos[motor_idx]
-                self.robot.msg.motor_cmd[motor_idx].q = (
-                    init_dof_pos[motor_idx] * (1 - alpha) + target_pos * alpha
-                )
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = self.robot.kp[motor_idx]
-                self.robot.msg.motor_cmd[motor_idx].kd = self.robot.kd[motor_idx]
-                self.robot.msg.motor_cmd[motor_idx].tau = 0
-            self.robot.msg.crc = self.robot.crc.Crc(self.robot.msg)
-            self.robot.lowcmd_publisher.Write(self.robot.msg)
-            time.sleep(self.robot.control_dt)
-        logger.info("Reached default position (legs only)")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="GR00T Locomotion Controller for Unitree G1")
-    parser.add_argument(
-        "--repo-id",
-        type=str,
-        default=DEFAULT_GROOT_REPO_ID,
-        help=f"Hugging Face Hub repo ID for GR00T policies (default: {DEFAULT_GROOT_REPO_ID})",
-    )
-    args = parser.parse_args()
-
-    # load policies
-    policy_balance, policy_walk = load_groot_policies(repo_id=args.repo_id)
-
-    # initialize robot
-    config = UnitreeG1Config()
-    robot = UnitreeG1(config)
-
-    # initialize gr00t locomotion controller
-    groot_controller = GrootLocomotionController(
-        policy_balance=policy_balance,
-        policy_walk=policy_walk,
-        robot=robot,
-        config=config,
-    )
-
-    # reset legs and start locomotion thread
-    try:
-        groot_controller.reset_robot()
-        groot_controller.start_locomotion_thread()
-
-        # log status
-        logger.info("Robot initialized with GR00T locomotion policies")
-        logger.info("Locomotion controller running in background thread")
-        logger.info("Press Ctrl+C to stop")
-
-        # keep robot alive
-        while True:
-            time.sleep(1.0)
-    except KeyboardInterrupt:
-        print("\nStopping locomotion...")
-        groot_controller.stop_locomotion_thread()
-        print("Done!")

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.4.3"
+version = "0.4.2"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 readme = "README.md"
 license = { text = "Apache-2.0" }
@@ -107,10 +107,6 @@ dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"]
 gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"]
 hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
 lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"]
-unitree_g1 = [
-    "pyzmq>=26.2.1,<28.0.0",
-    "onnxruntime>=1.16.0"
-]
 reachy2 = ["reachy2_sdk>=1.0.14,<1.1.0"]
 kinematics = ["lerobot[placo-dep]"]
 intelrealsense = [
@@ -133,7 +129,6 @@ groot = [
     "ninja>=1.11.1,<2.0.0",
     "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
-xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 
 # Features
@@ -162,7 +157,6 @@ all = [
     "lerobot[pi]",
     "lerobot[smolvla]",
     # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
-    "lerobot[xvla]",
     "lerobot[hilserl]",
     "lerobot[async]",
     "lerobot[dev]",
@@ -362,9 +356,9 @@ ignore_errors = false
 # module = "lerobot.async_inference.*"
 # ignore_errors = false
 
-[[tool.mypy.overrides]]
-module = "lerobot.transport.*"
-ignore_errors = false
+# [[tool.mypy.overrides]]
+# module = "lerobot.transport.*"
+# ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.scripts.*"

scripts/visualize_sarm_predictions.py

@@ -0,0 +1,761 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Inference script for SARM (Stage-Aware Reward Model).
+
+This script loads a trained SARM model and runs inference on a dataset episode,
+generating visualizations of the predicted task stages and progress over time.
+
+Example usage:
+    python scripts/visualize_sarm_predictions.py \
+        --model-id username/sarm-model \
+        --dataset-repo lerobot/aloha_sim_insertion_human \
+        --episode-index 0 \
+        --output-dir outputs/sarm_viz \
+        --task-description "insert the peg into the socket"
+"""
+
+import argparse
+import json
+import logging
+from pathlib import Path
+from typing import Optional
+
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import matplotlib.patches as mpatches
+import numpy as np
+import pandas as pd
+import torch
+from tqdm import tqdm
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.sarm.modeling_sarm import SARMRewardModel
+from lerobot.policies.sarm.sarm_utils import (
+    pad_state_to_max_dim,
+    compute_tau,
+    compute_cumulative_progress_batch,
+)
+from lerobot.datasets.utils import load_stats
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Run SARM inference and visualize predictions")
+    
+    # Model arguments
+    parser.add_argument(
+        "--model-id",
+        type=str,
+        required=True,
+        help="HuggingFace model ID or local path to trained SARM model"
+    )
+    
+    # Dataset arguments
+    parser.add_argument(
+        "--dataset-repo",
+        type=str,
+        required=True,
+        help="HuggingFace dataset repository ID (e.g., lerobot/aloha_sim_insertion_human)"
+    )
+    parser.add_argument(
+        "--episode-index",
+        type=int,
+        default=0,
+        help="Index of the episode to visualize (default: 0)"
+    )
+    parser.add_argument(
+        "--task-description",
+        type=str,
+        default="perform the task",
+        help="Task description for the reward model (default: 'perform the task')"
+    )
+    
+    # Output arguments
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="outputs/sarm_inference",
+        help="Directory to save visualization outputs (default: outputs/sarm_inference)"
+    )
+    parser.add_argument(
+        "--image-key",
+        type=str,
+        default=None,
+        help="Key for images in dataset (e.g., observation.images.image). If not specified, uses model config's image_key"
+    )
+    parser.add_argument(
+        "--state-key",
+        type=str,
+        default=None,
+        help="Key for joint states in dataset. If None, auto-detects from dataset"
+    )
+    
+    # Visualization options
+    parser.add_argument(
+        "--show-frames",
+        action="store_true",
+        help="Include sample frames in the visualization"
+    )
+    parser.add_argument(
+        "--num-sample-frames",
+        type=int,
+        default=8,
+        help="Number of sample frames to show (default: 8)"
+    )
+    parser.add_argument(
+        "--figsize",
+        type=int,
+        nargs=2,
+        default=[14, 8],
+        help="Figure size as width height (default: 14 8)"
+    )
+    
+    # Device
+    parser.add_argument(
+        "--device",
+        type=str,
+        default=None,
+        help="Device to run inference on (cuda/cpu, default: auto-detect)"
+    )
+    
+    return parser.parse_args()
+
+
+def load_episode_data(
+    dataset: LeRobotDataset,
+    episode_index: int,
+    image_key: str,
+    state_key: str | None = None
+) -> tuple[np.ndarray, np.ndarray, int, int, str]:
+    """
+    Load all frames and states from a specific episode.
+    
+    Args:
+        dataset: LeRobotDataset instance
+        episode_index: Index of the episode to load
+        image_key: Key for accessing images in the dataset
+        state_key: Key for accessing joint states (auto-detected if None)
+        
+    Returns:
+        Tuple of (frames, states, start_index, end_index, task_description)
+    """
+    # Get episode boundaries
+    episode_data = dataset.meta.episodes
+    start_idx = episode_data["dataset_from_index"][episode_index]
+    end_idx = episode_data["dataset_to_index"][episode_index]
+    
+    logger.info(f"Loading episode {episode_index}: frames {start_idx} to {end_idx} ({end_idx - start_idx} frames)")
+    
+    # Auto-detect state key if not provided
+    if state_key is None:
+        first_item = dataset[start_idx]
+        state_keys = [k for k in first_item.keys() if 'state' in k.lower() or 'qpos' in k.lower()]
+        if state_keys:
+            state_key = state_keys[0]
+            logger.info(f"Auto-detected state key: {state_key}")
+    
+    # Get task description from the dataset if available
+    task_description = None
+    first_item = dataset[start_idx]
+    if "task" in first_item:
+        task_description = first_item["task"]
+        logger.info(f"✓ Extracted task from episode {episode_index}: '{task_description}'")
+    
+    # Load all frames and states from the episode
+    frames = []
+    states = []
+    for idx in tqdm(range(start_idx, end_idx), desc="Loading frames"):
+        item = dataset[idx]
+        
+        # Get image
+        img = item[image_key]
+        
+        # Convert to numpy if needed
+        if isinstance(img, torch.Tensor):
+            img = img.cpu().numpy()
+        
+        # Handle different image formats (C, H, W) or (H, W, C)
+        if img.shape[0] in [1, 3]:  # Channel first
+            img = np.transpose(img, (1, 2, 0))
+        
+        # Convert to uint8 if needed
+        if img.dtype != np.uint8:
+            if img.max() <= 1.0:
+                img = (img * 255).astype(np.uint8)
+            else:
+                img = img.astype(np.uint8)
+        
+        frames.append(img)
+        
+        # Get state if available
+        if state_key and state_key in item:
+            state = item[state_key]
+            if isinstance(state, torch.Tensor):
+                state = state.cpu().numpy()
+            states.append(state)
+    
+    frames = np.array(frames)
+    states = np.array(states) if states else None
+    logger.info(f"Loaded {len(frames)} frames with shape {frames[0].shape}")
+    if states is not None:
+        logger.info(f"Loaded states with shape {states.shape}")
+    
+    return frames, states, start_idx, end_idx, task_description
+
+
+@torch.no_grad()
+def run_inference(
+    model: SARMRewardModel,
+    frames: np.ndarray,
+    states: Optional[np.ndarray],
+    task_description: str,
+    dataset_stats: dict | None = None,
+    state_key: str = "observation.state",
+    batch_size: int = 32
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Run SARM inference on video frames and joint states.
+    
+    (per SARM paper Section A.4):
+    - Frame 0: Initial frame of the episode (frame 0)
+    - Frames 1-8: 8 consecutive frames with frame_gap spacing ending at current frame t
+    Pattern: [frame_0, t-(7*gap), t-(6*gap), ..., t-gap, t]
+    
+    Args:
+        model: SARM model
+        frames: Video frames (num_frames, H, W, C) - all frames from ONE episode
+        states: Joint states (num_frames, state_dim)
+        task_description: Task description text
+        dataset_stats: Dataset statistics for state normalization (same as training)
+        state_key: Key for state in dataset_stats
+        batch_size: Batch size for processing slices
+        
+    Returns:
+        Tuple of (progress_predictions, stage_predictions)
+            - progress_predictions: (num_frames,)
+            - stage_predictions: (num_frames, num_stages)
+    """
+    logger.info("Encoding video frames with CLIP...")
+    video_embeddings = model.encode_images(frames)
+    
+    logger.info("Encoding task description with CLIP...")
+    text_embedding = model.encode_text(task_description)
+    
+    # Get config values
+    num_frames_model = model.config.num_frames  # 9
+    frame_gap = model.config.frame_gap  # 30
+    
+    logger.info("Creating video slices (SARM paper: initial frame + 8 consecutive)...")
+    
+    # Convert to tensors
+    video_embeddings = torch.tensor(video_embeddings, dtype=torch.float32)
+    text_embedding = torch.tensor(text_embedding, dtype=torch.float32)
+    if states is not None:
+        state_embeddings = torch.tensor(states, dtype=torch.float32)
+        
+        # Normalize states using dataset stats (same as training processor)
+        if dataset_stats is not None and state_key in dataset_stats:
+            mean = torch.tensor(dataset_stats[state_key]["mean"], dtype=torch.float32)
+            std = torch.tensor(dataset_stats[state_key]["std"], dtype=torch.float32)
+            state_embeddings = (state_embeddings - mean) / (std + 1e-8)
+            logger.info(f"✓ Applied MEAN_STD normalization to states using {state_key}")
+        else:
+            logger.warning("⚠ No dataset_stats provided - states not normalized (may differ from training)")
+    else:
+        state_embeddings = None
+    
+    video_slices = []
+    state_slices = []
+    
+    for current_frame in tqdm(range(len(video_embeddings)), desc="Creating slices"):
+        # Compute frame indices using symmetric bidirectional pattern:
+        # [initial (0), t-4*gap, t-3*gap, t-2*gap, t-gap, t, t+gap, t+2*gap, t+3*gap]
+        # Boundary handling: clamp to [0, last_valid]
+        deltas = model.config.observation_delta_indices
+        last_valid = len(video_embeddings) - 1
+        
+        frame_indices = []
+        for delta in deltas:
+            idx = current_frame + delta
+            idx = max(0, min(idx, last_valid))  # Clamp to valid range
+            frame_indices.append(idx)
+
+        video_slice = video_embeddings[frame_indices]
+        video_slices.append(video_slice)
+        
+        if state_embeddings is not None:
+            state_slice = state_embeddings[frame_indices]
+            state_slices.append(state_slice)
+    
+    video_slices = torch.stack(video_slices)  # (num_frames, num_frames_model, 512)
+    if state_embeddings is not None:
+        state_slices = torch.stack(state_slices)  # (num_frames, num_frames_model, state_dim)
+        # Pad states to max_state_dim (same as training processor)
+        state_slices = pad_state_to_max_dim(state_slices, model.config.max_state_dim)
+    else:
+        state_slices = None
+    
+    logger.info("Running SARM inference on all slices...")
+    # Process in batches
+    all_progress = []
+    all_stages = []
+    
+    for i in tqdm(range(0, len(video_slices), batch_size), desc="Inference"):
+        batch_video = video_slices[i:i + batch_size].to(model.device)
+        batch_states = state_slices[i:i + batch_size].to(model.device) if state_slices is not None else None
+        batch_size_actual = batch_video.shape[0]
+        
+        # Replicate text embedding for batch
+        batch_text = text_embedding.unsqueeze(0).repeat(batch_size_actual, 1).to(model.device)
+        
+        # Get predictions
+        stage_logits, stage_probs, progress_preds = model.sarm_transformer(
+            batch_video, batch_text, batch_states
+        )
+        
+        # Extract predictions at the "current frame" position
+        # With symmetric pattern [initial, t-4g, t-3g, t-2g, t-g, t, t+g, t+2g, t+3g],
+        # the current frame is at position 5 (0-indexed)
+        current_frame_idx = 5
+        batch_progress = progress_preds[:, current_frame_idx, 0].cpu().numpy()
+        batch_stages = stage_probs[:, current_frame_idx, :].cpu().numpy()
+        
+        all_progress.extend(batch_progress)
+        all_stages.extend(batch_stages)
+    
+    return np.array(all_progress), np.array(all_stages)
+
+
+def compute_ground_truth_progress(
+    dataset: LeRobotDataset,
+    episode_index: int,
+    temporal_proportions: dict[str, float],
+    subtask_names_ordered: list[str],
+) -> tuple[np.ndarray, np.ndarray] | tuple[None, None]:
+    """
+    Compute ground truth progress and stage labels for an episode using annotations.
+    
+    Uses SARM Paper Formula (2):
+        y_t = P_{k-1} + ᾱ_k × τ_t
+    
+    where:
+        - τ_t = (t - s_k) / (e_k - s_k) is within-subtask progress
+        - P_{k-1} is cumulative prior (sum of previous subtask proportions)
+        - ᾱ_k is the temporal proportion for subtask k
+    
+    Args:
+        dataset: LeRobotDataset instance
+        episode_index: Index of the episode
+        temporal_proportions: Dict mapping subtask name to proportion
+        subtask_names_ordered: Ordered list of subtask names (for consistent stage indexing)
+        
+    Returns:
+        Tuple of (ground_truth_progress, ground_truth_stages) arrays, or (None, None) if no annotations
+    """
+    # Load episode metadata
+    episodes_df = dataset.meta.episodes.to_pandas()
+    
+    # Check if annotations exist
+    if "subtask_names" not in episodes_df.columns:
+        logger.warning("No subtask_names column found in episodes metadata")
+        return None, None
+    
+    ep_subtask_names = episodes_df.loc[episode_index, "subtask_names"]
+    if ep_subtask_names is None or (isinstance(ep_subtask_names, float) and pd.isna(ep_subtask_names)):
+        logger.warning(f"No annotations found for episode {episode_index}")
+        return None, None
+    
+    subtask_start_frames = episodes_df.loc[episode_index, "subtask_start_frames"]
+    subtask_end_frames = episodes_df.loc[episode_index, "subtask_end_frames"]
+    
+    # Get episode boundaries
+    ep_start = dataset.meta.episodes["dataset_from_index"][episode_index]
+    ep_end = dataset.meta.episodes["dataset_to_index"][episode_index]
+    num_frames = ep_end - ep_start
+    
+    # Get temporal proportions as ordered list
+    temporal_proportions_list = [
+        temporal_proportions.get(name, 0.0) for name in subtask_names_ordered
+    ]
+    
+    logger.info(f"Computing ground truth for {num_frames} frames using {len(ep_subtask_names)} annotated subtasks")
+    logger.info(f"Subtask names in episode: {ep_subtask_names}")
+    logger.info(f"Subtask start frames: {subtask_start_frames}")
+    logger.info(f"Subtask end frames: {subtask_end_frames}")
+    logger.info(f"Temporal proportions (ordered): {dict(zip(subtask_names_ordered, temporal_proportions_list))}")
+    
+    # Compute ground truth for each frame
+    gt_progress = np.zeros(num_frames)
+    gt_stages = np.zeros(num_frames, dtype=np.int32)
+    
+    for frame_rel in range(num_frames):
+        # Find which subtask this frame belongs to
+        found = False
+        for j, (name, start_frame, end_frame) in enumerate(zip(ep_subtask_names, subtask_start_frames, subtask_end_frames)):
+            if frame_rel >= start_frame and frame_rel <= end_frame:
+                # Found the subtask - get its global index
+                stage_idx = subtask_names_ordered.index(name) if name in subtask_names_ordered else 0
+                
+                # Compute τ_t using utility function
+                tau = compute_tau(frame_rel, start_frame, end_frame)
+                
+                # Compute cumulative progress using utility function
+                progress = compute_cumulative_progress_batch(tau, stage_idx, temporal_proportions_list)
+                
+                gt_progress[frame_rel] = progress
+                gt_stages[frame_rel] = stage_idx
+                found = True
+                break
+        
+        if not found:
+            # Handle frames outside annotated subtasks
+            if frame_rel < subtask_start_frames[0]:
+                gt_progress[frame_rel] = 0.0
+                gt_stages[frame_rel] = 0
+            elif frame_rel > subtask_end_frames[-1]:
+                gt_progress[frame_rel] = 1.0
+                gt_stages[frame_rel] = len(subtask_names_ordered) - 1
+            else:
+                # Between subtasks - find previous subtask
+                for j in range(len(ep_subtask_names) - 1):
+                    if frame_rel > subtask_end_frames[j] and frame_rel < subtask_start_frames[j + 1]:
+                        name = ep_subtask_names[j]
+                        stage_idx = subtask_names_ordered.index(name) if name in subtask_names_ordered else j
+                        progress = compute_cumulative_progress_batch(1.0, stage_idx, temporal_proportions_list)
+                        gt_progress[frame_rel] = progress
+                        gt_stages[frame_rel] = stage_idx
+                        break
+    
+    logger.info(f"✓ Ground truth computed: final={gt_progress[-1]:.3f}, max={gt_progress.max():.3f}")
+    return gt_progress, gt_stages
+
+
+def visualize_predictions(
+    frames: np.ndarray,
+    progress_predictions: np.ndarray,
+    stage_predictions: np.ndarray,
+    task_description: str,
+    output_path: Path,
+    num_sample_frames: int = 8,
+    figsize: tuple = (14, 8),
+    subtask_names: list[str] | None = None,
+    temporal_proportions: dict[str, float] | None = None,
+    ground_truth_progress: np.ndarray | None = None,
+    ground_truth_stages: np.ndarray | None = None,
+):
+    """
+    Create visualization of SARM predictions with optional ground truth comparison.
+    
+    Args:
+        frames: Video frames (num_frames, H, W, C)
+        progress_predictions: Progress predictions (num_frames,)
+        stage_predictions: Stage probabilities (num_frames, num_stages)
+        task_description: Task description
+        output_path: Path to save the figure
+        num_sample_frames: Number of frames to show
+        figsize: Figure size (width, height)
+        subtask_names: Optional list of subtask names for labeling
+        temporal_proportions: Optional dict of temporal proportions for each subtask
+        ground_truth_progress: Optional ground truth progress array (num_frames,)
+        ground_truth_stages: Optional ground truth stage indices array (num_frames,)
+    """
+    num_stages = stage_predictions.shape[1]
+    stage_colors = plt.cm.tab10(np.linspace(0, 1, num_stages))
+    
+    # Use subtask names if available, otherwise use generic labels
+    if subtask_names is not None and len(subtask_names) == num_stages:
+        stage_labels = subtask_names
+    else:
+        stage_labels = [f'Stage {i+1}' for i in range(num_stages)]
+    
+    # Create figure with progress plot, stage plot, and sample frames
+    fig = plt.figure(figsize=(figsize[0], figsize[1] + 4))
+    gs = gridspec.GridSpec(3, 1, height_ratios=[2, 1, 1], hspace=0.3)
+    
+    ax_progress = fig.add_subplot(gs[0])
+    ax_stages = fig.add_subplot(gs[1], sharex=ax_progress)
+    ax_frames = fig.add_subplot(gs[2])
+    
+    frame_indices = np.arange(len(progress_predictions))
+    
+    # Plot 1: Progress over time
+    ax_progress.plot(frame_indices, progress_predictions, linewidth=2, color='#2E86AB', label='Predicted Progress')
+    ax_progress.fill_between(frame_indices, 0, progress_predictions, alpha=0.3, color='#2E86AB')
+    
+    # Plot ground truth if available
+    if ground_truth_progress is not None:
+        ax_progress.plot(frame_indices, ground_truth_progress, linewidth=2, color='#28A745', 
+                        linestyle='--', label='Ground Truth Progress')
+        ax_progress.fill_between(frame_indices, 0, ground_truth_progress, alpha=0.15, color='#28A745')
+    
+    ax_progress.axhline(y=1.0, color='gray', linestyle='--', alpha=0.5, linewidth=1)
+    ax_progress.set_ylabel('Task Progress', fontsize=12)
+    ax_progress.set_title(f'Task: "{task_description}"', fontsize=14, fontweight='bold')
+    ax_progress.grid(True, alpha=0.3)
+    ax_progress.set_ylim(-0.05, 1.1)
+    ax_progress.legend(loc='upper left')
+    
+    # Add statistics box
+    stats_text = (
+        f'Frames: {len(progress_predictions)}\n'
+        f'Final Progress: {progress_predictions[-1]:.3f}\n'
+        f'Max Progress: {progress_predictions.max():.3f}\n'
+        f'Mean Progress: {progress_predictions.mean():.3f}'
+    )
+    if ground_truth_progress is not None:
+        mse = np.mean((progress_predictions - ground_truth_progress) ** 2)
+        stats_text += f'\nMSE vs GT: {mse:.4f}'
+        stats_text += f'\nGT Final: {ground_truth_progress[-1]:.3f}'
+    
+    ax_progress.text(0.98, 0.02, stats_text, transform=ax_progress.transAxes,
+                    fontsize=10, verticalalignment='bottom', horizontalalignment='right',
+                    bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
+    
+    # Plot 2: Stage predictions (stacked area plot)
+    ax_stages.stackplot(frame_indices, *[stage_predictions[:, i] for i in range(num_stages)],
+                        colors=stage_colors, alpha=0.8, labels=stage_labels)
+    
+    # Plot ground truth stage as vertical bands or markers
+    if ground_truth_stages is not None:
+        # Find stage transition points in ground truth
+        stage_changes = np.where(np.diff(ground_truth_stages) != 0)[0] + 1
+        for change_idx in stage_changes:
+            ax_stages.axvline(x=change_idx, color='black', linestyle='-', alpha=0.7, linewidth=1.5)
+            ax_progress.axvline(x=change_idx, color='black', linestyle='-', alpha=0.3, linewidth=1)
+        
+        # Add small markers at bottom showing GT stage
+        gt_stage_normalized = ground_truth_stages / max(num_stages - 1, 1)
+        ax_stages.scatter(frame_indices[::30], np.zeros(len(frame_indices[::30])) + 0.02, 
+                         c=[stage_colors[s] for s in ground_truth_stages[::30]], 
+                         s=20, marker='|', alpha=0.8, label='GT Stage Markers')
+    
+    ax_stages.set_xlabel('Frame Index', fontsize=12)
+    ax_stages.set_ylabel('Stage Probability', fontsize=12)
+    ax_stages.set_ylim(0, 1)
+    ax_stages.grid(True, alpha=0.3)
+    
+    # Adjust legend based on number of stages and label lengths
+    if num_stages <= 5:
+        ax_stages.legend(loc='upper left', ncol=num_stages, fontsize=8)
+    else:
+        ax_stages.legend(loc='upper left', ncol=3, fontsize=7)
+    
+    # Add vertical lines and labels for expected stage transitions (if temporal proportions available)
+    if temporal_proportions is not None and subtask_names is not None:
+        cumulative_progress = 0.0
+        for i, name in enumerate(stage_labels):
+            if name in temporal_proportions:
+                # Find approximate frame where this stage should end
+                stage_end_progress = cumulative_progress + temporal_proportions[name]
+                
+                # Find frame index closest to this progress
+                progress_diffs = np.abs(progress_predictions - stage_end_progress)
+                stage_end_frame = np.argmin(progress_diffs)
+                
+                # Draw vertical line
+                ax_progress.axvline(x=stage_end_frame, color='gray', linestyle=':', alpha=0.5, linewidth=1)
+                ax_stages.axvline(x=stage_end_frame, color='gray', linestyle=':', alpha=0.5, linewidth=1)
+                
+                cumulative_progress = stage_end_progress
+    
+    # Plot 3: Sample frames (if requested)
+    frame_indices_to_show = np.linspace(0, len(frames) - 1, num_sample_frames, dtype=int)
+    
+    ax_frames.axis('off')
+    
+    # Create grid for frames
+    frame_height = frames[0].shape[0]
+    frame_width = frames[0].shape[1]
+    
+    combined_width = frame_width * num_sample_frames
+    combined_image = np.zeros((frame_height, combined_width, 3), dtype=np.uint8)
+    
+    for i, frame_idx in enumerate(frame_indices_to_show):
+        frame = frames[frame_idx]
+        if frame.shape[-1] == 1:
+            frame = np.repeat(frame, 3, axis=-1)
+        
+        # Add frame to combined image
+        x_start = i * frame_width
+        x_end = (i + 1) * frame_width
+        combined_image[:, x_start:x_end] = frame
+        
+        # Add frame number, progress, and stage
+        progress_val = progress_predictions[frame_idx]
+        stage_idx = np.argmax(stage_predictions[frame_idx])
+        stage_name = stage_labels[stage_idx] if stage_idx < len(stage_labels) else f'{stage_idx+1}'
+        
+        # Truncate long stage names for display
+        if len(stage_name) > 15:
+            stage_name = stage_name[:12] + '...'
+        
+        label = f'Frame {frame_idx}\nProg: {progress_val:.2f}\n{stage_name}'
+        
+        # Draw label on image
+        ax_frames.text(x_start + frame_width / 2, -10, label, 
+                      ha='center', va='top', fontsize=7, 
+                      bbox=dict(boxstyle='round', facecolor='white', alpha=0.7))
+    
+    ax_frames.imshow(combined_image)
+    ax_frames.set_title('Sample Frames', fontsize=12, pad=20)
+    
+    plt.tight_layout()
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    plt.savefig(output_path, dpi=150, bbox_inches='tight')
+    logger.info(f"Saved visualization to {output_path}")
+    
+    plt.close()
+
+
+def main():
+    args = parse_args()
+    
+    # Setup device
+    if args.device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    else:
+        device = args.device
+    logger.info(f"Using device: {device}")
+    
+    # Load model
+    logger.info(f"Loading SARM model from {args.model_id}...")
+    model = SARMRewardModel.from_pretrained(args.model_id)
+    model.to(device)
+    model.eval()
+    logger.info("Model loaded successfully")
+    
+    # Load dataset
+    logger.info(f"Loading dataset {args.dataset_repo}...")
+    dataset = LeRobotDataset(args.dataset_repo)
+    logger.info(f"Dataset loaded: {len(dataset.meta.episodes)} episodes, {len(dataset)} frames")
+    
+    # Validate episode index
+    if args.episode_index >= len(dataset.meta.episodes):
+        raise ValueError(
+            f"Episode index {args.episode_index} out of range. "
+            f"Dataset has {len(dataset.meta.episodes)} episodes."
+        )
+    
+    image_key = args.image_key if args.image_key is not None else model.config.image_key
+    state_key = args.state_key if args.state_key is not None else model.config.state_key
+    logger.info(f"Using image key: {image_key}")
+    logger.info(f"Using state key: {state_key}")
+    
+    # Load dataset stats for state normalization (same as training)
+    dataset_stats = load_stats(dataset.root)
+    if dataset_stats:
+        logger.info(f"✓ Loaded dataset stats from {dataset.root}")
+    else:
+        logger.warning("⚠ Could not load dataset stats - states will not be normalized")
+    
+    # Load episode data
+    frames, states, start_idx, end_idx, dataset_task = load_episode_data(
+        dataset, args.episode_index, image_key, state_key
+    )
+    
+    # Use task description from dataset if available, otherwise use command-line argument
+    task_description = dataset_task if dataset_task is not None else args.task_description
+    logger.info(f"Using task description: '{task_description}'")
+    
+    # Run inference
+    progress_predictions, stage_predictions = run_inference(
+        model, frames, states, task_description, 
+        dataset_stats=dataset_stats, state_key=state_key
+    )
+    
+    # Extract subtask names and temporal proportions from model config if available
+    subtask_names = None
+    temporal_proportions = None
+    
+    if hasattr(model.config, 'subtask_names') and model.config.subtask_names is not None:
+        subtask_names = model.config.subtask_names
+        logger.info(f"✓ Found {len(subtask_names)} subtask names in model config: {subtask_names}")
+    
+    # Try to load temporal proportions from model config
+    if hasattr(model.config, 'temporal_proportions') and model.config.temporal_proportions is not None:
+        temporal_proportions = {
+            name: prop for name, prop in zip(model.config.subtask_names, model.config.temporal_proportions)
+        }
+        logger.info(f"✓ Loaded temporal proportions from model config: {temporal_proportions}")
+    
+    # Fallback: try to load from dataset meta
+    if temporal_proportions is None:
+        proportions_path = dataset.root / "meta" / "temporal_proportions.json"
+        if proportions_path.exists():
+            with open(proportions_path, 'r') as f:
+                temporal_proportions = json.load(f)
+                logger.info(f"✓ Loaded temporal proportions from dataset: {temporal_proportions}")
+                
+                # Also extract subtask names from proportions if not already set
+                if subtask_names is None:
+                    subtask_names = sorted(temporal_proportions.keys())
+                    logger.info(f"✓ Extracted subtask names from proportions: {subtask_names}")
+    
+    # Compute ground truth progress if annotations are available
+    ground_truth_progress = None
+    ground_truth_stages = None
+    
+    if temporal_proportions is not None and subtask_names is not None:
+        logger.info("Attempting to compute ground truth progress from annotations...")
+        ground_truth_progress, ground_truth_stages = compute_ground_truth_progress(
+            dataset,
+            args.episode_index,
+            temporal_proportions,
+            subtask_names
+        )
+        if ground_truth_progress is None:
+            logger.warning("⚠ Ground truth not available - annotations may be missing for this episode")
+    else:
+        logger.warning("⚠ Cannot compute ground truth - temporal_proportions or subtask_names not available")
+    
+    output_dir = Path(args.output_dir)
+    output_path = output_dir / f"sarm_prediction_ep{args.episode_index}.png"
+    
+    visualize_predictions(
+        frames,
+        progress_predictions,
+        stage_predictions,
+        task_description,
+        output_path,
+        num_sample_frames=args.num_sample_frames,
+        figsize=tuple(args.figsize),
+        subtask_names=subtask_names,
+        temporal_proportions=temporal_proportions,
+        ground_truth_progress=ground_truth_progress,
+        ground_truth_stages=ground_truth_stages,
+    )
+    
+    predictions_path = output_dir / f"predictions_ep{args.episode_index}.npz"
+    save_dict = {
+        'progress': progress_predictions, 
+        'stages': stage_predictions
+    }
+    if ground_truth_progress is not None:
+        save_dict['gt_progress'] = ground_truth_progress
+        save_dict['gt_stages'] = ground_truth_stages
+    np.savez(predictions_path, **save_dict)
+    logger.info(f"Saved predictions to {predictions_path}")
+    logger.info(f"\nVisualization: {output_path}")
+
+
+if __name__ == "__main__":
+    main()
+

src/lerobot/configs/train.py

@@ -64,9 +64,26 @@ class TrainPipelineConfig(HubMixin):
     scheduler: LRSchedulerConfig | None = None
     eval: EvalConfig = field(default_factory=EvalConfig)
     wandb: WandBConfig = field(default_factory=WandBConfig)
-    checkpoint_path: Path | None = field(init=False, default=None)
+    
+    # RA-BC (Reward-Aligned Behavior Cloning) parameters
+    use_rabc: bool = False  # Enable reward-weighted training
+    reward_model_path: str | None = None  # Path to pre-trained reward model (e.g., SARM)
+    rabc_kappa: float = 0.01  # Hard threshold for high-quality samples
+    rabc_epsilon: float = 1e-6  # Small constant for numerical stability
+    rabc_update_freq: int = 1  # Compute rewards every N batches (1 = every batch)
+
     # Rename map for the observation to override the image and state keys
-    rename_map: dict[str, str] = field(default_factory=dict)
+    rename_map: dict[str, str] = field(default_factory=dict)       
+    checkpoint_path: Path | None = field(init=False, default=None)
+        
+
+    def validate(self):
+        # Validate RA-BC configuration
+        if self.use_rabc and not self.reward_model_path:
+            raise ValueError(
+                "RA-BC is enabled (use_rabc=True) but no reward_model_path provided. "
+                "Please specify a pre-trained reward model (e.g., SARM) path."
+            )
 
     def validate(self) -> None:
         # HACK: We parse again the cli args here to get the pretrained paths if there was some.

src/lerobot/datasets/aggregate.py

@@ -136,40 +136,21 @@ def update_meta_data(
         df["_orig_chunk"] = df[orig_chunk_col].copy()
         df["_orig_file"] = df[orig_file_col].copy()
 
-        # Get mappings for this video key
+        # Update chunk and file indices to point to destination
+        df[orig_chunk_col] = video_idx["chunk"]
+        df[orig_file_col] = video_idx["file"]
+
+        # Apply per-source-file timestamp offsets
         src_to_offset = video_idx.get("src_to_offset", {})
-        src_to_dst = video_idx.get("src_to_dst", {})
-
-        # Apply per-source-file mappings
-        if src_to_dst:
-            # Map each episode to its correct destination file and apply offset
+        if src_to_offset:
+            # Apply offset based on original source file
             for idx in df.index:
-                # Convert to Python int to avoid numpy type mismatch in dict lookup
-                src_key = (int(df.at[idx, "_orig_chunk"]), int(df.at[idx, "_orig_file"]))
-
-                # Get destination chunk/file for this source file
-                dst_chunk, dst_file = src_to_dst.get(src_key, (video_idx["chunk"], video_idx["file"]))
-                df.at[idx, orig_chunk_col] = dst_chunk
-                df.at[idx, orig_file_col] = dst_file
-
-                # Apply timestamp offset
-                offset = src_to_offset.get(src_key, 0)
-                df.at[idx, f"videos/{key}/from_timestamp"] += offset
-                df.at[idx, f"videos/{key}/to_timestamp"] += offset
-        elif src_to_offset:
-            # Fallback: use same destination for all, but apply per-file offsets
-            df[orig_chunk_col] = video_idx["chunk"]
-            df[orig_file_col] = video_idx["file"]
-            for idx in df.index:
-                # Convert to Python int to avoid numpy type mismatch in dict lookup
-                src_key = (int(df.at[idx, "_orig_chunk"]), int(df.at[idx, "_orig_file"]))
+                src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"])
                 offset = src_to_offset.get(src_key, 0)
                 df.at[idx, f"videos/{key}/from_timestamp"] += offset
                 df.at[idx, f"videos/{key}/to_timestamp"] += offset
         else:
             # Fallback to simple offset (for backward compatibility)
-            df[orig_chunk_col] = video_idx["chunk"]
-            df[orig_file_col] = video_idx["file"]
             df[f"videos/{key}/from_timestamp"] = (
                 df[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"]
             )
@@ -287,12 +268,6 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
         videos_idx[key]["episode_duration"] = 0
         # Track offset for each source (chunk, file) pair
         videos_idx[key]["src_to_offset"] = {}
-        # Track destination (chunk, file) for each source (chunk, file) pair
-        videos_idx[key]["src_to_dst"] = {}
-        # Initialize dst_file_durations if not present
-        # dst_file_durations tracks duration of each destination file
-        if "dst_file_durations" not in videos_idx[key]:
-            videos_idx[key]["dst_file_durations"] = {}
 
     for key, video_idx in videos_idx.items():
         unique_chunk_file_pairs = {
@@ -307,13 +282,9 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
 
         chunk_idx = video_idx["chunk"]
         file_idx = video_idx["file"]
-        dst_file_durations = video_idx["dst_file_durations"]
+        current_offset = video_idx["latest_duration"]
 
         for src_chunk_idx, src_file_idx in unique_chunk_file_pairs:
-            # Convert to Python int to ensure consistent dict keys
-            src_chunk_idx = int(src_chunk_idx)
-            src_file_idx = int(src_file_idx)
-
             src_path = src_meta.root / DEFAULT_VIDEO_PATH.format(
                 video_key=key,
                 chunk_index=src_chunk_idx,
@@ -327,17 +298,14 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
             )
 
             src_duration = get_video_duration_in_s(src_path)
-            dst_key = (chunk_idx, file_idx)
 
             if not dst_path.exists():
-                # New destination file: offset is 0
-                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = 0
-                videos_idx[key]["src_to_dst"][(src_chunk_idx, src_file_idx)] = dst_key
+                # Store offset before incrementing
+                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset
                 dst_path.parent.mkdir(parents=True, exist_ok=True)
                 shutil.copy(str(src_path), str(dst_path))
-                # Track duration of this destination file
-                dst_file_durations[dst_key] = src_duration
                 videos_idx[key]["episode_duration"] += src_duration
+                current_offset += src_duration
                 continue
 
             # Check file sizes before appending
@@ -345,11 +313,10 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
             dst_size = get_file_size_in_mb(dst_path)
 
             if dst_size + src_size >= video_files_size_in_mb:
-                # Rotate to a new file - offset is 0
-                chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, chunk_size)
-                dst_key = (chunk_idx, file_idx)
+                # Rotate to a new file, this source becomes start of new destination
+                # So its offset should be 0
                 videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = 0
-                videos_idx[key]["src_to_dst"][(src_chunk_idx, src_file_idx)] = dst_key
+                chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, chunk_size)
                 dst_path = dst_meta.root / DEFAULT_VIDEO_PATH.format(
                     video_key=key,
                     chunk_index=chunk_idx,
@@ -357,20 +324,16 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
                 )
                 dst_path.parent.mkdir(parents=True, exist_ok=True)
                 shutil.copy(str(src_path), str(dst_path))
-                # Track duration of this new destination file
-                dst_file_durations[dst_key] = src_duration
+                # Reset offset for next file
+                current_offset = src_duration
             else:
-                # Append to existing destination file
-                # Offset is the current duration of this destination file
-                current_dst_duration = dst_file_durations.get(dst_key, 0)
-                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_dst_duration
-                videos_idx[key]["src_to_dst"][(src_chunk_idx, src_file_idx)] = dst_key
+                # Append to existing video file - use current accumulated offset
+                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset
                 concatenate_video_files(
                     [dst_path, src_path],
                     dst_path,
                 )
-                # Update duration of this destination file
-                dst_file_durations[dst_key] = current_dst_duration + src_duration
+                current_offset += src_duration
 
             videos_idx[key]["episode_duration"] += src_duration
 

src/lerobot/datasets/dataset_tools.py

@@ -999,10 +999,18 @@ def _copy_data_with_feature_changes(
                     df[feature_name] = feature_values
                 else:
                     feature_slice = values[frame_idx:end_idx]
-                    if len(feature_slice.shape) > 1 and feature_slice.shape[1] == 1:
-                        df[feature_name] = feature_slice.flatten()
-                    else:
+                    if len(feature_slice.shape) == 1:
+                        # 1D array - can assign directly
                         df[feature_name] = feature_slice
+                    elif len(feature_slice.shape) == 2 and feature_slice.shape[1] == 1:
+                        # 2D array with single column - flatten it
+                        df[feature_name] = feature_slice.flatten()
+                    elif len(feature_slice.shape) == 2:
+                        # 2D array with multiple columns (e.g., embeddings) - convert to list of lists
+                        df[feature_name] = feature_slice.tolist()
+                    else:
+                        # Higher dimensional - convert to list
+                        df[feature_name] = [row.tolist() for row in feature_slice]
             frame_idx = end_idx
 
         # Write using the same chunk/file structure as source

src/lerobot/datasets/generating_embeddings/README.md

@@ -0,0 +1,146 @@
+# LeRobot Embedding Generation Script
+
+Generate embeddings for LeRobot datasets to make them more lightweight and efficient for training.
+
+## Overview
+
+This script processes v3.0 LeRobot datasets and adds pre-computed embeddings for:
+
+- **Task embeddings**: Language command embeddings using MiniLM
+- **Image embeddings**: Frame embeddings using DinoV2
+
+The resulting dataset can be used more efficiently during training by loading pre-computed embeddings instead of running encoders on-the-fly.
+
+## Supported Encoders
+
+### Image Encoders (DinoV2)
+
+DinoV2 is a self-supervised vision transformer that produces high-quality image embeddings:
+
+- **`dinov2_vits14`**: ViT-S/14 (384-dim) - Fastest, smaller model
+- **`dinov2_vitb14`**: ViT-B/14 (768-dim) - **Recommended** - Good balance
+- **`dinov2_vitl14`**: ViT-L/14 (1024-dim) - Best quality, slower
+
+### Language Encoders (MiniLM)
+
+MiniLM is a lightweight sentence transformer model:
+
+- **`minilm-l6`**: MiniLM-L6-v2 (384-dim) - Faster
+- **`minilm-l12`**: MiniLM-L12-v2 (384-dim) - **Recommended** - Better quality
+
+## Usage
+
+### Basic Command
+
+```bash
+python src/lerobot/datasets/generating_embeddings/generate_embeddings.py \
+    --repo-id lerobot/utokyo_xarm_bimanual \
+    --output-repo-id your-username/utokyo_xarm_bimanual_embeddings \
+    --image-encoder dinov2_vitb14 \
+    --language-encoder minilm-l12 \
+    --push-to-hub
+```
+
+### Lightweight Version (No Videos)
+
+Removes video files to significantly reduce storage:
+
+```bash
+python src/lerobot/datasets/generating_embeddings/generate_embeddings.py \
+    --repo-id lerobot/utokyo_xarm_bimanual \
+    --output-repo-id your-username/utokyo_xarm_bimanual_lightweight \
+    --image-encoder dinov2_vitb14 \
+    --language-encoder minilm-l12 \
+    --remove-videos \
+    --push-to-hub
+```
+
+## Output
+
+The script adds new features to your dataset:
+
+### New Features
+
+1. **`task_embedding`**: Language embedding for each frame
+   - Shape: `[384]` (MiniLM)
+   - One embedding per frame based on its task
+
+2. **`{camera_key}_embedding`**: Image embedding for each camera view
+   - Shape: `[384]`, `[768]`, or `[1024]` depending on DinoV2 model
+   - Examples: `observation.images.top_embedding`, `observation.images.wrist_embedding`
+
+### Using Embeddings in Training
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+# Load dataset with embeddings
+dataset = LeRobotDataset("your-username/utokyo_xarm_bimanual_embeddings")
+
+# Access embeddings
+item = dataset[0]
+task_emb = item["task_embedding"]  # Shape: [384]
+img_emb = item["observation.images.top_embedding"]  # Shape: [768]
+
+# Use in your policy
+# Instead of running encoders during training, use pre-computed embeddings
+```
+
+## Extending with New Encoders
+
+The script is designed to be easily extensible. To add a new encoder:
+
+### 1. Create Encoder Class
+
+```python
+class MyCustomImageEncoder(ImageEncoder):
+    """Your custom image encoder."""
+
+    def __init__(self, device: str = "cuda"):
+        super().__init__(device)
+        # Load your model
+        self.model = load_my_model()
+        self.model = self.model.to(self.device)
+        self.model.eval()
+
+    def encode(self, images: list[np.ndarray]) -> np.ndarray:
+        """Encode a batch of images."""
+        # Your encoding logic here
+        embeddings = []
+        for img in images:
+            emb = self.model(img)
+            embeddings.append(emb)
+        return np.array(embeddings)
+
+    @property
+    def embedding_dim(self) -> int:
+        """Return embedding dimension."""
+        return 512  # Your embedding dimension
+```
+
+### 2. Add to Factory Function
+
+```python
+def get_image_encoder(encoder_name: str, device: str = "cuda") -> ImageEncoder:
+    encoders = {
+        "dinov2_vits14": lambda: DinoV2Encoder(model_name="dinov2_vits14", device=device),
+        "dinov2_vitb14": lambda: DinoV2Encoder(model_name="dinov2_vitb14", device=device),
+        "dinov2_vitl14": lambda: DinoV2Encoder(model_name="dinov2_vitl14", device=device),
+        # Add your encoder
+        "my_custom": lambda: MyCustomImageEncoder(device=device),
+    }
+    # ... rest of function
+```
+
+## Validating Embeddings
+
+After generating embeddings, you can validate them using `validate_embeddings.py`:
+
+```bash
+python src/lerobot/datasets/generating_embeddings/validate_embeddings.py \
+    --original-repo-id lerobot/utokyo_xarm_bimanual \
+    --embeddings-repo-id pepijn223/utokyo_xarm_bimanual_embeddings \
+    --image-encoder dinov2_vitb14 \
+    --language-encoder minilm-l12 \
+    --num-samples 20
+```

src/lerobot/datasets/generating_embeddings/encoders.py

@@ -0,0 +1,147 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+
+import numpy as np
+import torch
+from PIL import Image
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class ImageEncoder:
+    """Base class for image encoders."""
+
+    def __init__(self, device: str = "cuda"):
+        self.device = torch.device(device if torch.cuda.is_available() else "cpu")
+
+    def encode(self, images: list[np.ndarray]) -> np.ndarray:
+        """Encode a batch of images."""
+        raise NotImplementedError
+
+
+class DinoV2Encoder(ImageEncoder):
+    """DinoV2 image encoder.
+
+    DinoV2 is a self-supervised vision transformer that produces high-quality image embeddings.
+    Supports multiple model sizes (ViT-S/14, ViT-B/14, ViT-L/14).
+    """
+
+    def __init__(self, model_name: str = "dinov2_vitb14", device: str = "cuda", batch_size: int = 32):
+        super().__init__(device)
+        self.batch_size = batch_size
+        self.model_name = model_name
+        logger.info(f"Loading DinoV2 model: {model_name}")
+        self.model = torch.hub.load("facebookresearch/dinov2", model_name)  # nosec B614
+        self.model = self.model.to(self.device)
+        self.model.eval()
+
+        # DinoV2 preprocessing
+        from torchvision import transforms
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            ]
+        )
+
+    def encode(self, images: list[np.ndarray]) -> np.ndarray:
+        """Encode a batch of images."""
+        embeddings = []
+
+        with torch.inference_mode():
+            for i in range(0, len(images), self.batch_size):
+                batch_images = images[i : i + self.batch_size]
+                # Convert numpy arrays to PIL Images and apply transforms
+                pil_images = [Image.fromarray(img.astype(np.uint8)) for img in batch_images]
+                tensors = torch.stack([self.transform(img) for img in pil_images]).to(self.device)
+
+                # Get embeddings
+                batch_embeddings = self.model(tensors).cpu().numpy()
+                embeddings.append(batch_embeddings)
+
+        return np.concatenate(embeddings, axis=0)
+
+    @property
+    def embedding_dim(self) -> int:
+        """Return the embedding dimension based on model size."""
+        if "vits14" in self.model_name:
+            return 384  # DinoV2 ViT-S/14
+        elif "vitb14" in self.model_name:
+            return 768  # DinoV2 ViT-B/14
+        elif "vitl14" in self.model_name:
+            return 1024  # DinoV2 ViT-L/14
+        else:
+            return 768  # Default to ViT-B/14
+
+
+class LanguageEncoder:
+    """Base class for language encoders."""
+
+    def __init__(self, device: str = "cuda"):
+        self.device = torch.device(device if torch.cuda.is_available() else "cpu")
+
+    def encode(self, texts: list[str]) -> np.ndarray:
+        """Encode a batch of texts."""
+        raise NotImplementedError
+
+
+class MiniLMEncoder(LanguageEncoder):
+    """MiniLM language encoder.
+
+    MiniLM is a lightweight sentence transformer model that produces high-quality text embeddings.
+    Supports L6 and L12 model sizes.
+    """
+
+    def __init__(self, model_name: str = "sentence-transformers/all-MiniLM-L12-v2", device: str = "cuda"):
+        super().__init__(device)
+        self.model_name = model_name
+        logger.info(f"Loading MiniLM model: {model_name}")
+
+        from transformers import AutoModel, AutoTokenizer
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModel.from_pretrained(model_name).to(self.device)
+        self.model.eval()
+
+    def _mean_pooling(self, model_output, attention_mask):
+        """Mean pooling to get sentence embeddings."""
+        token_embeddings = model_output[0]
+        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+            input_mask_expanded.sum(1), min=1e-9
+        )
+
+    def encode(self, texts: list[str]) -> np.ndarray:
+        """Encode a batch of texts."""
+        with torch.inference_mode():
+            encoded_input = self.tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+            encoded_input = {k: v.to(self.device) for k, v in encoded_input.items()}
+
+            model_output = self.model(**encoded_input)
+            embeddings = self._mean_pooling(model_output, encoded_input["attention_mask"])
+
+            return embeddings.cpu().numpy()
+
+    @property
+    def embedding_dim(self) -> int:
+        """Return the embedding dimension."""
+        return 384  # Both MiniLM-L6 and L12 output 384-dim embeddings

src/lerobot/datasets/generating_embeddings/generate_embeddings.py

@@ -0,0 +1,329 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Generate embeddings for LeRobot datasets to make them more lightweight and efficient.
+
+This script:
+1. Loads a v3.0 LeRobot dataset from the hub
+2. Computes embeddings for tasks (language commands) and frames (images)
+3. Stores embeddings as new features in the dataset
+4. Optionally removes video files to reduce size
+5. Pushes the converted dataset to the hub
+
+Current supported encoders:
+- Image: DinoV2 (dinov2_vits14, dinov2_vitb14, dinov2_vitl14)
+- Language: MiniLM (minilm-l6, minilm-l12)
+
+The architecture is extensible - you can add more encoders by:
+1. Creating a new encoder class inheriting from ImageEncoder or LanguageEncoder
+2. Implementing the encode() method and embedding_dim property
+3. Adding it to the get_image_encoder() or get_language_encoder() factory function
+
+Usage example:
+    python src/lerobot/datasets/generating_embeddings/generate_embeddings.py \
+        --repo-id lerobot/utokyo_xarm_bimanual \
+        --output-repo-id lerobot/utokyo_xarm_bimanual_embeddings \
+        --image-encoder dinov2_vitb14 \
+        --language-encoder minilm-l12 \
+        --remove-videos \
+        --push-to-hub
+"""
+
+import argparse
+import shutil
+from pathlib import Path
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from lerobot.datasets.generating_embeddings.encoders import (
+    DinoV2Encoder,
+    ImageEncoder,
+    LanguageEncoder,
+    MiniLMEncoder,
+)
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+
+def get_image_encoder(encoder_name: str, device: str = "cuda") -> ImageEncoder:
+    """Factory function to get image encoder.
+
+    To add a new encoder:
+    1. Create a new class inheriting from ImageEncoder
+    2. Implement encode() and embedding_dim property
+    3. Add it to the encoders dictionary below
+    """
+    encoders = {
+        "dinov2_vits14": lambda: DinoV2Encoder(model_name="dinov2_vits14", device=device),
+        "dinov2_vitb14": lambda: DinoV2Encoder(model_name="dinov2_vitb14", device=device),
+        "dinov2_vitl14": lambda: DinoV2Encoder(model_name="dinov2_vitl14", device=device),
+    }
+
+    if encoder_name not in encoders:
+        raise ValueError(f"Unknown image encoder: {encoder_name}. Available options: {list(encoders.keys())}")
+
+    return encoders[encoder_name]()
+
+
+def get_language_encoder(encoder_name: str, device: str = "cuda") -> LanguageEncoder:
+    """Factory function to get language encoder.
+
+    To add a new encoder:
+    1. Create a new class inheriting from LanguageEncoder
+    2. Implement encode() and embedding_dim property
+    3. Add it to the encoders dictionary below
+    """
+    encoders = {
+        "minilm-l6": lambda: MiniLMEncoder(
+            model_name="sentence-transformers/all-MiniLM-L6-v2", device=device
+        ),
+        "minilm-l12": lambda: MiniLMEncoder(
+            model_name="sentence-transformers/all-MiniLM-L12-v2", device=device
+        ),
+    }
+
+    if encoder_name not in encoders:
+        raise ValueError(
+            f"Unknown language encoder: {encoder_name}. Available options: {list(encoders.keys())}"
+        )
+
+    return encoders[encoder_name]()
+
+
+def generate_embeddings_for_dataset(
+    repo_id: str,
+    output_repo_id: str,
+    image_encoder: ImageEncoder,
+    language_encoder: LanguageEncoder,
+    remove_videos: bool = False,
+    local_dir: Path | None = None,
+    output_local_dir: Path | None = None,
+    push_to_hub: bool = False,
+):
+    """Generate embeddings for a LeRobot dataset.
+
+    Args:
+        repo_id: Source dataset repository ID
+        output_repo_id: Output dataset repository ID
+        image_encoder: Image encoder instance
+        language_encoder: Language encoder instance
+        remove_videos: Whether to remove video files
+        local_dir: Local directory for source dataset
+        output_local_dir: Local directory for output dataset
+        push_to_hub: Whether to push to hub after conversion
+    """
+    from lerobot.datasets.dataset_tools import modify_features
+
+    print(f"Loading dataset: {repo_id}")
+
+    dataset = LeRobotDataset(repo_id, root=local_dir, download_videos=True)
+    print(f"Dataset: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+
+    print("Computing task embeddings...")
+    unique_tasks = dataset.meta.tasks.index.tolist()
+    task_embeddings = {}
+
+    for task in tqdm(unique_tasks, desc="Encoding tasks"):
+        # Clean up task text
+        task_clean = task.strip().capitalize().strip(" .,!?-_")
+        embedding = language_encoder.encode([task_clean])[0]
+        task_embeddings[task] = embedding
+
+    print(f"Computed {len(task_embeddings)} task embeddings")
+
+    print("Processing frames and computing embeddings...")
+    all_task_embeddings = []
+    all_image_embeddings_dict = {cam_key: [] for cam_key in dataset.meta.camera_keys}
+
+    for frame_idx in tqdm(range(dataset.num_frames), desc="Processing frames"):
+        item = dataset.hf_dataset[frame_idx]
+        ep_idx = item["episode_index"].item()
+
+        task = dataset.meta.tasks.iloc[item["task_index"].item()].name
+        task_emb = task_embeddings[task]
+        all_task_embeddings.append(task_emb)
+
+        for cam_key in dataset.meta.camera_keys:
+            if cam_key in dataset.meta.video_keys:
+                current_ts = item["timestamp"].item()
+                video_frames = dataset._query_videos({cam_key: [current_ts]}, ep_idx)
+                img = video_frames[cam_key]
+
+                if isinstance(img, torch.Tensor):
+                    if img.ndim == 4:
+                        img = img[0]  # (T, C, H, W) -> (C, H, W)
+                    elif img.ndim != 3:
+                        raise ValueError(f"Unexpected video frame shape {img.shape} for camera {cam_key}")
+                    img_np = (img.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
+                else:
+                    img_np = np.array(img)
+            else:
+                img = item[cam_key]
+                if isinstance(img, torch.Tensor):
+                    if img.ndim == 3:
+                        img_np = (img.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
+                    else:
+                        raise ValueError(f"Unexpected image shape {img.shape} for camera {cam_key}")
+                else:
+                    img_np = np.array(img)
+
+            all_image_embeddings_dict[cam_key].append(img_np)
+
+    print("Computing image embeddings...")
+    image_embeddings_dict = {}
+    for cam_key, images in all_image_embeddings_dict.items():
+        print(f"  {cam_key}: {len(images)} images")
+        embeddings = image_encoder.encode(images)
+        image_embeddings_dict[cam_key] = embeddings
+
+    all_task_embeddings = np.array(all_task_embeddings)
+    for cam_key in dataset.meta.camera_keys:
+        image_embeddings_dict[cam_key] = np.array(image_embeddings_dict[cam_key])
+
+    img_emb_dim = image_encoder.embedding_dim
+    lang_emb_dim = language_encoder.embedding_dim
+
+    add_features_dict = {
+        "task_embedding": (
+            all_task_embeddings,
+            {"dtype": "float32", "shape": [lang_emb_dim], "names": None},
+        ),
+    }
+
+    for cam_key in dataset.meta.camera_keys:
+        add_features_dict[f"{cam_key}_embedding"] = (
+            image_embeddings_dict[cam_key],
+            {"dtype": "float32", "shape": [img_emb_dim], "names": None},
+        )
+
+    print("Adding embeddings to dataset...")
+    remove_features_list = None
+    if remove_videos:
+        remove_features_list = dataset.meta.video_keys
+
+    output_dataset = modify_features(
+        dataset=dataset,
+        add_features=add_features_dict,
+        remove_features=remove_features_list,
+        output_dir=output_local_dir,
+        repo_id=output_repo_id,
+    )
+
+    if remove_videos:
+        print("Removing video files...")
+        videos_dir = output_dataset.root / "videos"
+        if videos_dir.exists():
+            shutil.rmtree(videos_dir)
+
+    print(f"Saved to: {output_dataset.root}")
+
+    if push_to_hub:
+        print(f"Pushing to hub: {output_repo_id}")
+        output_dataset.push_to_hub(push_videos=not remove_videos)
+        print("Done!")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate embeddings for LeRobot datasets",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Basic usage with default encoders (DinoV2 ViT-B/14 + MiniLM-L12)
+  python src/lerobot/datasets/generating_embeddings/generate_embeddings.py \\
+      --repo-id lerobot/utokyo_xarm_bimanual \\
+      --output-repo-id your-username/utokyo_xarm_bimanual_embeddings \\
+      --image-encoder dinov2_vitb14 \\
+      --language-encoder minilm-l12 \\
+      --push-to-hub
+
+  # Generate embeddings and remove videos
+  python src/lerobot/datasets/generating_embeddings/generate_embeddings.py \\
+      --repo-id lerobot/utokyo_xarm_bimanual \\
+      --output-repo-id your-username/utokyo_xarm_bimanual_lightweight \\
+      --image-encoder dinov2_vitb14 \\
+      --language-encoder minilm-l12 \\
+      --remove-videos \\
+      --push-to-hub
+
+Available image encoders:
+  - dinov2_vits14: DinoV2 ViT-S/14 (384-dim, faster)
+  - dinov2_vitb14: DinoV2 ViT-B/14 (768-dim, recommended)
+  - dinov2_vitl14: DinoV2 ViT-L/14 (1024-dim, best quality)
+
+Available language encoders:
+  - minilm-l6: MiniLM-L6-v2 (384-dim, faster)
+  - minilm-l12: MiniLM-L12-v2 (384-dim, recommended)
+        """,
+    )
+    parser.add_argument("--repo-id", type=str, required=True, help="Source dataset repository ID")
+    parser.add_argument("--output-repo-id", type=str, required=True, help="Output dataset repository ID")
+    parser.add_argument(
+        "--image-encoder",
+        type=str,
+        default="dinov2_vitb14",
+        help="Image encoder to use (default: dinov2_vitb14)",
+    )
+    parser.add_argument(
+        "--language-encoder",
+        type=str,
+        default="minilm-l12",
+        help="Language encoder to use (default: minilm-l12)",
+    )
+    parser.add_argument(
+        "--remove-videos",
+        action="store_true",
+        help="Remove video files after generating embeddings",
+    )
+    parser.add_argument("--local-dir", type=str, default=None, help="Local directory for source dataset")
+    parser.add_argument(
+        "--output-local-dir", type=str, default=None, help="Local directory for output dataset"
+    )
+    parser.add_argument(
+        "--push-to-hub",
+        action="store_true",
+        help="Push the converted dataset to the hub",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda",
+        help="Device to use for encoding (default: cuda)",
+    )
+
+    args = parser.parse_args()
+
+    # Load encoders
+    image_encoder = get_image_encoder(args.image_encoder, device=args.device)
+    language_encoder = get_language_encoder(args.language_encoder, device=args.device)
+
+    # Generate embeddings
+    generate_embeddings_for_dataset(
+        repo_id=args.repo_id,
+        output_repo_id=args.output_repo_id,
+        image_encoder=image_encoder,
+        language_encoder=language_encoder,
+        remove_videos=args.remove_videos,
+        local_dir=Path(args.local_dir) if args.local_dir else None,
+        output_local_dir=Path(args.output_local_dir) if args.output_local_dir else None,
+        push_to_hub=args.push_to_hub,
+    )
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/datasets/generating_embeddings/validate_embeddings.py

@@ -0,0 +1,222 @@
+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Validate pre-computed embeddings against on-the-fly computed embeddings.
+
+Usage:
+    python src/lerobot/datasets/generating_embeddings/validate_embeddings.py \
+        --original-repo-id lerobot/utokyo_xarm_bimanual \
+        --embeddings-repo-id <your_username>/utokyo_xarm_bimanual_embeddings \
+        --image-encoder dinov2_vitb14 \
+        --language-encoder minilm-l12 \
+        --num-samples 10
+"""
+
+import argparse
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from lerobot.datasets.generating_embeddings.encoders import ImageEncoder, LanguageEncoder
+from lerobot.datasets.generating_embeddings.generate_embeddings import (
+    get_image_encoder,
+    get_language_encoder,
+)
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+
+def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+    """Compute cosine similarity between two vectors."""
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+
+def validate_embeddings(
+    original_repo_id: str,
+    embeddings_repo_id: str,
+    image_encoder: ImageEncoder,
+    language_encoder: LanguageEncoder,
+    num_samples: int = 10,
+    device: str = "cuda",
+):
+    """Validate pre-computed embeddings against on-the-fly embeddings.
+
+    Args:
+        original_repo_id: Original dataset repository ID
+        embeddings_repo_id: Dataset with pre-computed embeddings repository ID
+        image_encoder: Image encoder instance
+        language_encoder: Language encoder instance
+        num_samples: Number of samples to validate
+        device: Device to use for encoding
+    """
+    # Load both datasets
+    print("Loading datasets...")
+    original_dataset = LeRobotDataset(original_repo_id, download_videos=True)
+    embeddings_dataset = LeRobotDataset(embeddings_repo_id, download_videos=False)
+
+    # Verify both datasets have the same number of frames
+    assert original_dataset.num_frames == embeddings_dataset.num_frames, (
+        f"Frame count mismatch: original={original_dataset.num_frames}, "
+        f"embeddings={embeddings_dataset.num_frames}"
+    )
+
+    camera_keys = original_dataset.meta.camera_keys
+
+    # Check embedding features exist
+    expected_features = ["task_embedding"] + [f"{cam}_embedding" for cam in camera_keys]
+    for feat in expected_features:
+        if feat not in embeddings_dataset.features:
+            raise ValueError(f"Embedding feature not found: {feat}")
+
+    # Select random sample indices
+    sample_indices = np.random.choice(
+        original_dataset.num_frames, size=min(num_samples, original_dataset.num_frames), replace=False
+    )
+    print(f"Validating {len(sample_indices)} samples...")
+
+    # Track statistics
+    task_similarities = []
+    image_similarities = {cam: [] for cam in camera_keys}
+
+    for idx in tqdm(sample_indices, desc="Validating"):
+        idx = int(idx)
+
+        embeddings_item = embeddings_dataset[idx]
+        precomputed_task_emb = embeddings_item["task_embedding"].numpy()
+        precomputed_image_embs = {cam: embeddings_item[f"{cam}_embedding"].numpy() for cam in camera_keys}
+
+        original_item = original_dataset[idx]
+
+        # Get task and compute embedding
+        task = original_item["task"]
+        # Clean up task text (same as in generate_embeddings.py)
+        task_clean = task.strip().capitalize().strip(" .,!?-_")
+        onthefly_task_emb = language_encoder.encode([task_clean])[0]
+
+        # Get images and compute embeddings
+        onthefly_image_embs = {}
+        for cam in camera_keys:
+            img = original_item[cam]
+            # Convert to numpy if needed
+            if isinstance(img, torch.Tensor):
+                if img.ndim == 3:  # (C, H, W)
+                    img_np = (img.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
+                else:
+                    raise ValueError(f"Unexpected image shape: {img.shape}")
+            else:
+                img_np = np.array(img)
+
+            onthefly_image_embs[cam] = image_encoder.encode([img_np])[0]
+
+        # Task embedding comparison
+        task_sim = cosine_similarity(precomputed_task_emb, onthefly_task_emb)
+        task_similarities.append(task_sim)
+
+        # Image embedding comparison
+        for cam in camera_keys:
+            img_sim = cosine_similarity(precomputed_image_embs[cam], onthefly_image_embs[cam])
+            image_similarities[cam].append(img_sim)
+
+    # Results
+    print("\nResults:")
+    task_sim_threshold = 0.99
+    img_sim_threshold = 0.99
+
+    task_mean_sim = np.mean(task_similarities)
+    task_pass = task_mean_sim >= task_sim_threshold
+
+    print(f"  Task: {task_mean_sim:.4f} {'✓' if task_pass else '✗'}")
+
+    for cam in camera_keys:
+        cam_mean_sim = np.mean(image_similarities[cam])
+        cam_pass = cam_mean_sim >= img_sim_threshold
+        print(f"  {cam}: {cam_mean_sim:.4f} {'✓' if cam_pass else '✗'}")
+
+    image_pass = all(np.mean(image_similarities[cam]) >= img_sim_threshold for cam in camera_keys)
+
+    print()
+    if task_pass and image_pass:
+        print("✓ PASSED")
+    else:
+        print("✗ FAILED")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Validate and compare pre-computed embeddings with on-the-fly embeddings",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Example:
+  python src/lerobot/datasets/generating_embeddings/validate_embeddings.py \\
+      --original-repo-id lerobot/utokyo_xarm_bimanual \\
+      --embeddings-repo-id lerobot/utokyo_xarm_bimanual_embeddings \\
+      --image-encoder dinov2_vitb14 \\
+      --language-encoder minilm-l12 \\
+      --num-samples 20
+        """,
+    )
+    parser.add_argument("--original-repo-id", type=str, required=True, help="Original dataset repository ID")
+    parser.add_argument(
+        "--embeddings-repo-id",
+        type=str,
+        required=True,
+        help="Dataset with pre-computed embeddings repository ID",
+    )
+    parser.add_argument(
+        "--image-encoder",
+        type=str,
+        default="dinov2_vitb14",
+        help="Image encoder to use (default: dinov2_vitb14)",
+    )
+    parser.add_argument(
+        "--language-encoder",
+        type=str,
+        default="minilm-l12",
+        help="Language encoder to use (default: minilm-l12)",
+    )
+    parser.add_argument(
+        "--num-samples",
+        type=int,
+        default=10,
+        help="Number of samples to validate (default: 10)",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda",
+        help="Device to use for encoding (default: cuda)",
+    )
+
+    args = parser.parse_args()
+
+    # Load encoders
+    image_encoder = get_image_encoder(args.image_encoder, device=args.device)
+    language_encoder = get_language_encoder(args.language_encoder, device=args.device)
+
+    # Validate embeddings
+    validate_embeddings(
+        original_repo_id=args.original_repo_id,
+        embeddings_repo_id=args.embeddings_repo_id,
+        image_encoder=image_encoder,
+        language_encoder=language_encoder,
+        num_samples=args.num_samples,
+        device=args.device,
+    )
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/datasets/image_writer.py

@@ -110,8 +110,8 @@ def worker_thread_loop(queue: queue.Queue):
         if item is None:
             queue.task_done()
             break
-        image_array, fpath, compress_level = item
-        write_image(image_array, fpath, compress_level)
+        image_array, fpath = item
+        write_image(image_array, fpath)
         queue.task_done()
 
 
@@ -169,13 +169,11 @@ class AsyncImageWriter:
                 p.start()
                 self.processes.append(p)
 
-    def save_image(
-        self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1
-    ):
+    def save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path):
         if isinstance(image, torch.Tensor):
             # Convert tensor to numpy array to minimize main process time
             image = image.cpu().numpy()
-        self.queue.put((image, fpath, compress_level))
+        self.queue.put((image, fpath))
 
     def wait_until_done(self):
         self.queue.join()

src/lerobot/datasets/lerobot_dataset.py

@@ -13,7 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import concurrent.futures
 import contextlib
 import logging
 import shutil
@@ -540,15 +539,6 @@ class LeRobotDatasetMetadata:
         return obj
 
 
-def _encode_video_worker(video_key: str, episode_index: int, root: Path, fps: int) -> Path:
-    temp_path = Path(tempfile.mkdtemp(dir=root)) / f"{video_key}_{episode_index:03d}.mp4"
-    fpath = DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=episode_index, frame_index=0)
-    img_dir = (root / fpath).parent
-    encode_video_frames(img_dir, temp_path, fps, overwrite=True)
-    shutil.rmtree(img_dir)
-    return temp_path
-
-
 class LeRobotDataset(torch.utils.data.Dataset):
     def __init__(
         self,
@@ -1081,7 +1071,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             ep_buffer[key] = current_ep_idx if key == "episode_index" else []
         return ep_buffer
 
-    # TODO(Steven): consider move this to utils
     def _get_image_file_path(self, episode_index: int, image_key: str, frame_index: int) -> Path:
         fpath = DEFAULT_IMAGE_PATH.format(
             image_key=image_key, episode_index=episode_index, frame_index=frame_index
@@ -1091,15 +1080,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def _get_image_file_dir(self, episode_index: int, image_key: str) -> Path:
         return self._get_image_file_path(episode_index, image_key, frame_index=0).parent
 
-    def _save_image(
-        self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1
-    ) -> None:
+    def _save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path) -> None:
         if self.image_writer is None:
             if isinstance(image, torch.Tensor):
                 image = image.cpu().numpy()
-            write_image(image, fpath, compress_level=compress_level)
+            write_image(image, fpath)
         else:
-            self.image_writer.save_image(image=image, fpath=fpath, compress_level=compress_level)
+            self.image_writer.save_image(image=image, fpath=fpath)
 
     def add_frame(self, frame: dict) -> None:
         """
@@ -1137,19 +1124,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 )
                 if frame_index == 0:
                     img_path.parent.mkdir(parents=True, exist_ok=True)
-                compress_level = 1 if self.features[key]["dtype"] == "video" else 6
-                self._save_image(frame[key], img_path, compress_level)
+                self._save_image(frame[key], img_path)
                 self.episode_buffer[key].append(str(img_path))
             else:
                 self.episode_buffer[key].append(frame[key])
 
         self.episode_buffer["size"] += 1
 
-    def save_episode(
-        self,
-        episode_data: dict | None = None,
-        parallel_encoding: bool = True,
-    ) -> None:
+    def save_episode(self, episode_data: dict | None = None) -> None:
         """
         This will save to disk the current episode in self.episode_buffer.
 
@@ -1161,8 +1143,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             episode_data (dict | None, optional): Dict containing the episode data to save. If None, this will
                 save the current episode in self.episode_buffer, which is filled with 'add_frame'. Defaults to
                 None.
-            parallel_encoding (bool, optional): If True, encode videos in parallel using ProcessPoolExecutor.
-                Defaults to True on Linux, False on macOS as it tends to use all the CPU available already.
         """
         episode_buffer = episode_data if episode_data is not None else self.episode_buffer
 
@@ -1199,40 +1179,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         use_batched_encoding = self.batch_encoding_size > 1
 
         if has_video_keys and not use_batched_encoding:
-            num_cameras = len(self.meta.video_keys)
-            if parallel_encoding and num_cameras > 1:
-                # TODO(Steven): Ideally we would like to control the number of threads per encoding such that:
-                # num_cameras * num_threads = (total_cpu -1)
-                with concurrent.futures.ProcessPoolExecutor(max_workers=num_cameras) as executor:
-                    future_to_key = {
-                        executor.submit(
-                            _encode_video_worker,
-                            video_key,
-                            episode_index,
-                            self.root,
-                            self.fps,
-                        ): video_key
-                        for video_key in self.meta.video_keys
-                    }
-
-                    results = {}
-                    for future in concurrent.futures.as_completed(future_to_key):
-                        video_key = future_to_key[future]
-                        try:
-                            temp_path = future.result()
-                            results[video_key] = temp_path
-                        except Exception as exc:
-                            logging.error(f"Video encoding failed for {video_key}: {exc}")
-                            raise exc
-
-                for video_key in self.meta.video_keys:
-                    temp_path = results[video_key]
-                    ep_metadata.update(
-                        self._save_episode_video(video_key, episode_index, temp_path=temp_path)
-                    )
-            else:
-                for video_key in self.meta.video_keys:
-                    ep_metadata.update(self._save_episode_video(video_key, episode_index))
+            for video_key in self.meta.video_keys:
+                ep_metadata.update(self._save_episode_video(video_key, episode_index))
 
         # `meta.save_episode` need to be executed after encoding the videos
         self.meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats, ep_metadata)
@@ -1397,18 +1345,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         return metadata
 
-    def _save_episode_video(
-        self,
-        video_key: str,
-        episode_index: int,
-        temp_path: Path | None = None,
-    ) -> dict:
+    def _save_episode_video(self, video_key: str, episode_index: int) -> dict:
         # Encode episode frames into a temporary video
-        if temp_path is None:
-            ep_path = self._encode_temporary_episode_video(video_key, episode_index)
-        else:
-            ep_path = temp_path
-
+        ep_path = self._encode_temporary_episode_video(video_key, episode_index)
         ep_size_in_mb = get_file_size_in_mb(ep_path)
         ep_duration_in_s = get_video_duration_in_s(ep_path)
 
@@ -1526,7 +1465,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
         since video encoding with ffmpeg is already using multithreading.
         """
-        return _encode_video_worker(video_key, episode_index, self.root, self.fps)
+        temp_path = Path(tempfile.mkdtemp(dir=self.root)) / f"{video_key}_{episode_index:03d}.mp4"
+        img_dir = self._get_image_file_dir(episode_index, video_key)
+        encode_video_frames(img_dir, temp_path, self.fps, overwrite=True)
+        shutil.rmtree(img_dir)
+        return temp_path
 
     @classmethod
     def create(

src/lerobot/datasets/temporal_sampler.py

@@ -0,0 +1,151 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+SARM Temporal Sampler for reward model training.
+
+Samples frames uniformly from episodes for SARM's 9-frame symmetric pattern:
+- 1 initial frame + 4 frames before + current + 3 frames after
+
+Boundary handling: clamp to first/last frame when indices go out of bounds.
+This enables truly uniform sampling across entire episodes.
+"""
+
+import logging
+from typing import Iterator, Optional
+import numpy as np
+import torch
+from torch.utils.data import Sampler
+import random
+
+
+class SARMTemporalSampler(Sampler):
+    """
+    Temporal sampler for SARM reward model training with symmetric/bidirectional sampling.
+    
+    SARM uses 9 frames per sample:
+    - Frame 0: Initial frame of the episode (always frame 0)
+    - Frames 1-8: Symmetric context around current frame
+      Pattern: [t-4*gap, t-3*gap, t-2*gap, t-gap, t, t+gap, t+2*gap, t+3*gap]
+    
+    Boundary handling:
+    - Early frames: backward indices clamp to 0 (e.g., [0,0,0,5,35,65,95,125])
+    - Late frames: forward indices clamp to last frame (e.g., [850,880,910,940,970,1000,1000,1000])
+    
+    This enables truly uniform sampling across entire episodes.
+    
+    Args:
+        dataset_from_index: Start indices of episodes (global dataset indices)
+        dataset_to_index: End indices of episodes (global dataset indices)
+        frame_gap: Gap between consecutive frames (default: 30 = 1 second at 30fps)
+        shuffle: Whether to shuffle sampling order
+        seed: Random seed for reproducibility
+        samples_per_epoch: Number of samples per epoch (default: 6400)
+        min_episode_length: Minimum episode length to include (default: 1)
+    """
+    
+    def __init__(
+        self,
+        dataset_from_index: np.ndarray,
+        dataset_to_index: np.ndarray,
+        frame_gap: int = 30,
+        shuffle: bool = True,
+        seed: Optional[int] = None,
+        samples_per_epoch: int = 6400,
+        min_episode_length: int = 1,
+    ):
+        self.dataset_from_index = np.array(dataset_from_index)
+        self.dataset_to_index = np.array(dataset_to_index)
+        self.frame_gap = frame_gap
+        self.shuffle = shuffle
+        self.samples_per_epoch = samples_per_epoch
+        self.min_episode_length = min_episode_length
+        
+        if seed is not None:
+            self.seed = seed
+            random.seed(seed)
+            np.random.seed(seed)
+            self.generator = torch.Generator().manual_seed(seed)
+        else:
+            self.generator = torch.Generator()
+        
+        # Compute valid episodes and sampling positions (ALL frames for uniform sampling)
+        self._compute_valid_positions()
+        
+        logging.info(
+            f"SARMTemporalSampler: {len(self.valid_episodes)} valid episodes, "
+            f"{len(self.all_valid_positions)} positions (uniform sampling), "
+            f"{self.samples_per_epoch} samples per epoch, "
+            f"frame_gap={frame_gap}, symmetric bidirectional pattern"
+        )
+    
+    def _compute_valid_positions(self):
+        """Compute valid episodes and ALL sampling positions for uniform sampling.
+        
+        With symmetric bidirectional sampling, we can sample from ANY frame:
+        - Early frames: backward indices clamp to first frame
+        - Late frames: forward indices clamp to last frame
+        """
+        self.valid_episodes = []
+        self.all_valid_positions = []
+        
+        for ep_idx in range(len(self.dataset_from_index)):
+            ep_start = self.dataset_from_index[ep_idx]
+            ep_end = self.dataset_to_index[ep_idx]
+            episode_length = ep_end - ep_start
+            
+            # Include all episodes with at least min_episode_length frames
+            if episode_length >= self.min_episode_length:
+                self.valid_episodes.append((ep_idx, ep_start, ep_end))
+                
+                # Include ALL positions in the episode (truly uniform sampling)
+                for pos in range(ep_start, ep_end):
+                    self.all_valid_positions.append(pos)
+        
+        self.valid_episodes = np.array(self.valid_episodes)
+        self.all_valid_positions = np.array(self.all_valid_positions)
+        
+        if len(self.all_valid_positions) == 0:
+            raise ValueError(
+                f"No valid sampling positions found! "
+                f"Check that episodes have at least {self.min_episode_length} frames."
+            )
+    
+    def __len__(self) -> int:
+        return self.samples_per_epoch
+    
+    def __iter__(self) -> Iterator[int]:
+        """
+        Yields global dataset indices for uniform sampling across episodes.
+        
+        Each yielded index represents the "current frame" position.
+        The dataset's observation_delta_indices then handles loading:
+        - Frame 0: Episode initial frame (via large negative delta clamping)
+        - Frames 1-8: Symmetric context around current frame (with boundary clamping)
+        
+        For early frames: backward indices clamp to first frame (progress ~0%)
+        For late frames: forward indices clamp to last frame (progress ~100%)
+        """
+        if self.shuffle:
+            # Randomly sample from all valid positions
+            for _ in range(self.samples_per_epoch):
+                idx = np.random.randint(0, len(self.all_valid_positions))
+                yield int(self.all_valid_positions[idx])
+        else:
+            # Sequential sampling with wrap-around
+            for i in range(self.samples_per_epoch):
+                idx = i % len(self.all_valid_positions)
+                yield int(self.all_valid_positions[idx])

src/lerobot/datasets/utils.py

@@ -49,7 +49,7 @@ from lerobot.utils.utils import SuppressProgressBars, is_valid_numpy_dtype_strin
 
 DEFAULT_CHUNK_SIZE = 1000  # Max number of files per chunk
 DEFAULT_DATA_FILE_SIZE_IN_MB = 100  # Max size per file
-DEFAULT_VIDEO_FILE_SIZE_IN_MB = 200  # Max size per file
+DEFAULT_VIDEO_FILE_SIZE_IN_MB = 500  # Max size per file
 
 INFO_PATH = "meta/info.json"
 STATS_PATH = "meta/stats.json"

src/lerobot/datasets/video_utils.py

@@ -311,7 +311,6 @@ def encode_video_frames(
     fast_decode: int = 0,
     log_level: int | None = av.logging.ERROR,
     overwrite: bool = False,
-    preset: int | None = None,
 ) -> None:
     """More info on ffmpeg arguments tuning on `benchmark/video/README.md`"""
     # Check encoder availability
@@ -360,9 +359,6 @@ def encode_video_frames(
         value = f"fast-decode={fast_decode}" if vcodec == "libsvtav1" else "fastdecode"
         video_options[key] = value
 
-    if vcodec == "libsvtav1":
-        video_options["preset"] = str(preset) if preset is not None else "12"
-
     # Set logging level
     if log_level is not None:
         # "While less efficient, it is generally preferable to modify logging with Python's logging"

src/lerobot/envs/configs.py

@@ -245,7 +245,7 @@ class HILSerlRobotEnvConfig(EnvConfig):
 class LiberoEnv(EnvConfig):
     task: str = "libero_10"  # can also choose libero_spatial, libero_object, etc.
     fps: int = 30
-    episode_length: int | None = None
+    episode_length: int = 520
     obs_type: str = "pixels_agent_pos"
     render_mode: str = "rgb_array"
     camera_name: str = "agentview_image,robot0_eye_in_hand_image"
@@ -272,7 +272,6 @@ class LiberoEnv(EnvConfig):
             LIBERO_KEY_PIXELS_EYE_IN_HAND: f"{OBS_IMAGES}.image2",
         }
     )
-    control_mode: str = "relative"  # or "absolute"
 
     def __post_init__(self):
         if self.obs_type == "pixels":

src/lerobot/envs/factory.py

@@ -19,10 +19,8 @@ from typing import Any
 import gymnasium as gym
 from gymnasium.envs.registration import registry as gym_registry
 
-from lerobot.configs.policies import PreTrainedConfig
 from lerobot.envs.configs import AlohaEnv, EnvConfig, LiberoEnv, PushtEnv
 from lerobot.envs.utils import _call_make_env, _download_hub_file, _import_hub_module, _normalize_hub_result
-from lerobot.policies.xvla.configuration_xvla import XVLAConfig
 from lerobot.processor import ProcessorStep
 from lerobot.processor.env_processor import LiberoProcessorStep
 from lerobot.processor.pipeline import PolicyProcessorPipeline
@@ -41,7 +39,6 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
 
 def make_env_pre_post_processors(
     env_cfg: EnvConfig,
-    policy_cfg: PreTrainedConfig,
 ) -> tuple[
     PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
@@ -64,10 +61,6 @@ def make_env_pre_post_processors(
     # Preprocessor and Postprocessor steps are Identity for most environments
     preprocessor_steps: list[ProcessorStep] = []
     postprocessor_steps: list[ProcessorStep] = []
-    if isinstance(policy_cfg, XVLAConfig):
-        from lerobot.policies.xvla.processor_xvla import make_xvla_libero_pre_post_processors
-
-        return make_xvla_libero_pre_post_processors()
 
     # For LIBERO environments, add the LiberoProcessorStep to preprocessor
     if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
@@ -143,8 +136,6 @@ def make_env(
             init_states=cfg.init_states,
             gym_kwargs=cfg.gym_kwargs,
             env_cls=env_cls,
-            control_mode=cfg.control_mode,
-            episode_length=cfg.episode_length,
         )
     elif "metaworld" in cfg.type:
         from lerobot.envs.metaworld import create_metaworld_envs

src/lerobot/envs/libero.py

@@ -80,7 +80,10 @@ def get_libero_dummy_action():
     return [0, 0, 0, 0, 0, 0, -1]
 
 
+OBS_STATE_DIM = 8
 ACTION_DIM = 7
+AGENT_POS_LOW = -1000.0
+AGENT_POS_HIGH = 1000.0
 ACTION_LOW = -1.0
 ACTION_HIGH = 1.0
 TASK_SUITE_MAX_STEPS: dict[str, int] = {
@@ -100,7 +103,6 @@ class LiberoEnv(gym.Env):
         task_suite: Any,
         task_id: int,
         task_suite_name: str,
-        episode_length: int | None = None,
         camera_name: str | Sequence[str] = "agentview_image,robot0_eye_in_hand_image",
         obs_type: str = "pixels",
         render_mode: str = "rgb_array",
@@ -112,7 +114,6 @@ class LiberoEnv(gym.Env):
         episode_index: int = 0,
         camera_name_mapping: dict[str, str] | None = None,
         num_steps_wait: int = 10,
-        control_mode: str = "relative",
     ):
         super().__init__()
         self.task_id = task_id
@@ -140,19 +141,14 @@ class LiberoEnv(gym.Env):
         self.camera_name_mapping = camera_name_mapping
         self.num_steps_wait = num_steps_wait
         self.episode_index = episode_index
-        self.episode_length = episode_length
         # Load once and keep
         self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None
         self._init_state_id = self.episode_index  # tie each sub-env to a fixed init state
 
         self._env = self._make_envs_task(task_suite, self.task_id)
         default_steps = 500
-        self._max_episode_steps = (
-            TASK_SUITE_MAX_STEPS.get(task_suite_name, default_steps)
-            if self.episode_length is None
-            else self.episode_length
-        )
-        self.control_mode = control_mode
+        self._max_episode_steps = TASK_SUITE_MAX_STEPS.get(task_suite_name, default_steps)
+
         images = {}
         for cam in self.camera_name:
             images[self.camera_name_mapping[cam]] = spaces.Box(
@@ -300,15 +296,6 @@ class LiberoEnv(gym.Env):
         # Increasing this value can improve determinism and reproducibility across resets.
         for _ in range(self.num_steps_wait):
             raw_obs, _, _, _ = self._env.step(get_libero_dummy_action())
-
-        if self.control_mode == "absolute":
-            for robot in self._env.robots:
-                robot.controller.use_delta = False
-        elif self.control_mode == "relative":
-            for robot in self._env.robots:
-                robot.controller.use_delta = True
-        else:
-            raise ValueError(f"Invalid control mode: {self.control_mode}")
         observation = self._format_raw_obs(raw_obs)
         info = {"is_success": False}
         return observation, info
@@ -354,10 +341,8 @@ def _make_env_fns(
     task_id: int,
     n_envs: int,
     camera_names: list[str],
-    episode_length: int | None,
     init_states: bool,
     gym_kwargs: Mapping[str, Any],
-    control_mode: str,
 ) -> list[Callable[[], LiberoEnv]]:
     """Build n_envs factory callables for a single (suite, task_id)."""
 
@@ -369,9 +354,7 @@ def _make_env_fns(
             task_suite_name=suite_name,
             camera_name=camera_names,
             init_states=init_states,
-            episode_length=episode_length,
             episode_index=episode_index,
-            control_mode=control_mode,
             **local_kwargs,
         )
 
@@ -391,8 +374,6 @@ def create_libero_envs(
     camera_name: str | Sequence[str] = "agentview_image,robot0_eye_in_hand_image",
     init_states: bool = True,
     env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
-    control_mode: str = "relative",
-    episode_length: int | None = None,
 ) -> dict[str, dict[int, Any]]:
     """
     Create vectorized LIBERO environments with a consistent return shape.
@@ -434,14 +415,12 @@ def create_libero_envs(
         for tid in selected:
             fns = _make_env_fns(
                 suite=suite,
-                episode_length=episode_length,
                 suite_name=suite_name,
                 task_id=tid,
                 n_envs=n_envs,
                 camera_names=camera_names,
                 init_states=init_states,
                 gym_kwargs=gym_kwargs,
-                control_mode=control_mode,
             )
             out[suite_name][tid] = env_cls(fns)
             print(f"Built vec env | suite={suite_name} | task_id={tid} | n_envs={n_envs}")

src/lerobot/optim/optimizers.py

@@ -104,107 +104,6 @@ class SGDConfig(OptimizerConfig):
         return torch.optim.SGD(params, **kwargs)
 
 
-@OptimizerConfig.register_subclass("xvla-adamw")
-@dataclass
-class XVLAAdamWConfig(OptimizerConfig):
-    """Custom AdamW optimizer for XVLA with differential learning rates.
-
-    The Vision-Language Model (VLM) is trained with 1/10 of the base learning rate
-    for stable optimization, while all other components use the full LR.
-
-    This LR ratio is crucial for achieving strong and stable finetuning performance.
-
-    Soft-prompts can optionally use a separate learning rate with warm-up support.
-    Set `soft_prompt_lr_scale` to a value < 1.0 (e.g., 0.1) to start soft-prompts
-    at a lower LR. Combine with a warmup scheduler for optimal results.
-
-    Note:
-        Completely matching official reported performance may require an additional
-        warm-up LR schedule for soft-prompts, which can bring minor improvements.
-        When `soft_prompt_warmup_lr_scale` is set, soft-prompts start at
-        `lr * soft_prompt_warmup_lr_scale` and should be warmed up via the scheduler.
-
-    Parameter Groups:
-        - Group 0 (vlm): VLM parameters at lr * 0.1, weight_decay * 0.1
-        - Group 1 (soft_prompts): Soft-prompt parameters at lr * soft_prompt_lr_scale
-        - Group 2 (other): All other parameters at full lr
-    """
-
-    lr: float = 1e-4
-    betas: tuple[float, float] = (0.9, 0.99)
-    eps: float = 1e-8
-    weight_decay: float = 0.0
-    grad_clip_norm: float = 10.0
-    # Soft-prompt specific settings
-    soft_prompt_lr_scale: float = 1.0  # Scale factor for soft-prompt LR (1.0 = same as base LR)
-    soft_prompt_warmup_lr_scale: float | None = None  # If set, start soft-prompts at this scale (e.g., 0.01)
-
-    def build(self, params: dict) -> torch.optim.Optimizer:
-        """
-        Build AdamW optimizer with differential learning rates.
-
-        Expects `named_parameters()` as input (dict of name -> param).
-        Applies:
-        - lr * 0.1 for all VLM-related parameters
-        - lr * soft_prompt_lr_scale for soft-prompt parameters (with optional warmup)
-        - full lr for all other parameters
-
-        Args:
-            params: Dictionary of parameter names to parameters (from named_parameters())
-
-        Returns:
-            AdamW optimizer with parameter groups for VLM, soft-prompts, and other components
-        """
-        assert isinstance(params, dict), "Custom LR optimizer requires `named_parameters()` as inputs."
-
-        vlm_group, soft_prompt_group, other_group = [], [], []
-        for name, p in params.items():
-            if not p.requires_grad:
-                continue
-            if "vlm" in name.lower():
-                vlm_group.append(p)
-            elif "soft_prompt" in name.lower():
-                soft_prompt_group.append(p)
-            else:
-                other_group.append(p)
-
-        # Determine soft-prompt LR
-        soft_prompt_lr = self.lr * self.soft_prompt_lr_scale
-        if self.soft_prompt_warmup_lr_scale is not None:
-            # Start at warmup scale, scheduler will warm up to soft_prompt_lr
-            soft_prompt_lr = self.lr * self.soft_prompt_warmup_lr_scale
-
-        param_groups = [
-            {
-                "params": vlm_group,
-                "lr": self.lr * 0.1,
-                "weight_decay": self.weight_decay * 0.1,
-                "name": "vlm",
-            },
-            {
-                "params": soft_prompt_group,
-                "lr": soft_prompt_lr,
-                "weight_decay": self.weight_decay,
-                "name": "soft_prompts",
-            },
-            {
-                "params": other_group,
-                "lr": self.lr,
-                "weight_decay": self.weight_decay,
-                "name": "other",
-            },
-        ]
-
-        # Filter out empty groups
-        param_groups = [g for g in param_groups if len(g["params"]) > 0]
-
-        return torch.optim.AdamW(
-            param_groups,
-            betas=self.betas,
-            eps=self.eps,
-        )
-
-
 @OptimizerConfig.register_subclass("multi_adam")
 @dataclass
 class MultiAdamConfig(OptimizerConfig):

src/lerobot/policies/__init__.py

@@ -21,7 +21,6 @@ from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig
 from .smolvla.processor_smolvla import SmolVLANewLineProcessor
 from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig
 from .vqbet.configuration_vqbet import VQBeTConfig as VQBeTConfig
-from .xvla.configuration_xvla import XVLAConfig as XVLAConfig
 
 __all__ = [
     "ACTConfig",
@@ -32,5 +31,4 @@ __all__ = [
     "TDMPCConfig",
     "VQBeTConfig",
     "GrootConfig",
-    "XVLAConfig",
 ]

src/lerobot/policies/factory.py

@@ -16,7 +16,6 @@
 
 from __future__ import annotations
 
-import importlib
 import logging
 from typing import Any, TypedDict
 
@@ -36,12 +35,12 @@ from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.sac.configuration_sac import SACConfig
+from lerobot.policies.sarm.configuration_sarm import SARMConfig
 from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
 from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
 from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.policies.utils import validate_visual_features_consistency
 from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
-from lerobot.policies.xvla.configuration_xvla import XVLAConfig
 from lerobot.processor import PolicyAction, PolicyProcessorPipeline
 from lerobot.processor.converters import (
     batch_to_transition,
@@ -105,19 +104,16 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 
         return SmolVLAPolicy
+    elif name == "sarm":
+        from lerobot.policies.sarm.modeling_sarm import SARMRewardModel
+
+        return SARMRewardModel
     elif name == "groot":
         from lerobot.policies.groot.modeling_groot import GrootPolicy
 
         return GrootPolicy
-    elif name == "xvla":
-        from lerobot.policies.xvla.modeling_xvla import XVLAPolicy
-
-        return XVLAPolicy
     else:
-        try:
-            return _get_policy_cls_from_policy_name(name=name)
-        except Exception as e:
-            raise ValueError(f"Policy type '{name}' is not available.") from e
+        raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
 
 def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
@@ -159,14 +155,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return RewardClassifierConfig(**kwargs)
     elif policy_type == "groot":
         return GrootConfig(**kwargs)
-    elif policy_type == "xvla":
-        return XVLAConfig(**kwargs)
     else:
-        try:
-            config_cls = PreTrainedConfig.get_choice_class(policy_type)
-            return config_cls(**kwargs)
-        except Exception as e:
-            raise ValueError(f"Policy type '{policy_type}' is not available.") from e
+        raise ValueError(f"Policy type '{policy_type}' is not available.")
 
 
 class ProcessorConfigKwargs(TypedDict, total=False):
@@ -337,6 +327,14 @@ def make_pre_post_processors(
             dataset_stats=kwargs.get("dataset_stats"),
         )
 
+    elif isinstance(policy_cfg, SARMConfig):
+        from lerobot.policies.sarm.processor_sarm import make_sarm_pre_post_processors
+
+        processors = make_sarm_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+            dataset_meta=kwargs.get("dataset_meta"),
+        )
     elif isinstance(policy_cfg, GrootConfig):
         from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors
 
@@ -344,24 +342,9 @@ def make_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
         )
-    elif isinstance(policy_cfg, XVLAConfig):
-        from lerobot.policies.xvla.processor_xvla import (
-            make_xvla_pre_post_processors,
-        )
-
-        processors = make_xvla_pre_post_processors(
-            config=policy_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
 
     else:
-        try:
-            processors = _make_processors_from_policy_config(
-                config=policy_cfg,
-                dataset_stats=kwargs.get("dataset_stats"),
-            )
-        except Exception as e:
-            raise ValueError(f"Processor for policy type '{policy_cfg.type}' is not implemented.") from e
+        raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.")
 
     return processors
 
@@ -430,10 +413,18 @@ def make_policy(
             raise ValueError("env_cfg cannot be None when ds_meta is not provided")
         features = env_to_policy_features(env_cfg)
 
-    cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
+    if not cfg.output_features:
+        cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
     if not cfg.input_features:
         cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
     kwargs["config"] = cfg
+    
+    # Pass dataset_stats to the policy if available (needed for some policies like SARM)
+    if ds_meta is not None and hasattr(ds_meta, 'stats'):
+        kwargs["dataset_stats"] = ds_meta.stats
+    
+    if ds_meta is not None:
+        kwargs["dataset_meta"] = ds_meta
 
     if cfg.pretrained_path:
         # Load a pretrained policy and override the config if needed (for example, if there are inference-time
@@ -454,65 +445,3 @@ def make_policy(
         # TODO: (jadechoghari) - add a check_state(cfg, features) and check_action(cfg, features)
 
     return policy
-
-
-def _get_policy_cls_from_policy_name(name: str) -> type[PreTrainedConfig]:
-    """Get policy class from its registered name using dynamic imports.
-
-    This is used as a helper function to import policies from 3rd party lerobot plugins.
-
-    Args:
-        name: The name of the policy.
-    Returns:
-        The policy class corresponding to the given name.
-    """
-    if name not in PreTrainedConfig.get_known_choices():
-        raise ValueError(
-            f"Unknown policy name '{name}'. Available policies: {PreTrainedConfig.get_known_choices()}"
-        )
-
-    config_cls = PreTrainedConfig.get_choice_class(name)
-    config_cls_name = config_cls.__name__
-
-    model_name = config_cls_name.removesuffix("Config")  # e.g., DiffusionConfig -> Diffusion
-    if model_name == config_cls_name:
-        raise ValueError(
-            f"The config class name '{config_cls_name}' does not follow the expected naming convention."
-            f"Make sure it ends with 'Config'!"
-        )
-    cls_name = model_name + "Policy"  # e.g., DiffusionConfig -> DiffusionPolicy
-    module_path = config_cls.__module__.replace(
-        "configuration_", "modeling_"
-    )  # e.g., configuration_diffusion -> modeling_diffusion
-
-    module = importlib.import_module(module_path)
-    policy_cls = getattr(module, cls_name)
-    return policy_cls
-
-
-def _make_processors_from_policy_config(
-    config: PreTrainedConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[Any, Any]:
-    """Create pre- and post-processors from a policy configuration using dynamic imports.
-
-    This is used as a helper function to import processor factories from 3rd party lerobot plugins.
-
-    Args:
-        config: The policy configuration object.
-        dataset_stats: Dataset statistics for normalization.
-    Returns:
-        A tuple containing the input (pre-processor) and output (post-processor) pipelines.
-    """
-
-    policy_type = config.type
-    function_name = f"make_{policy_type}_pre_post_processors"
-    module_path = config.__class__.__module__.replace(
-        "configuration_", "processor_"
-    )  # e.g., configuration_diffusion -> processor_diffusion
-    logging.debug(
-        f"Instantiating pre/post processors using function '{function_name}' from module '{module_path}'"
-    )
-    module = importlib.import_module(module_path)
-    function = getattr(module, function_name)
-    return function(config, dataset_stats=dataset_stats)

src/lerobot/policies/pi0/modeling_pi0.py

@@ -812,13 +812,16 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         )
 
         dt = -1.0 / num_steps
+        dt = torch.tensor(dt, dtype=torch.float32, device=device)
 
         x_t = noise
-        for step in range(num_steps):
-            time = 1.0 + step * dt
-            time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)
+        time = torch.tensor(1.0, dtype=torch.float32, device=device)
+        while time >= -dt / 2:
+            expanded_time = time.expand(bsize)
 
-            def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
+            # Define a closure function to properly capture expanded_time
+            # This avoids the lambda expression (E731) and loop variable binding (B023) issues
+            def denoise_step_partial_call(input_x_t, current_timestep=expanded_time):
                 return self.denoise_step(
                     state=state,
                     prefix_pad_masks=prefix_pad_masks,
@@ -843,11 +846,15 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             else:
                 v_t = denoise_step_partial_call(x_t)
 
-            x_t = x_t + dt * v_t
+            # Euler step
+            x_t += dt * v_t
 
+            # Record x_t and v_t after Euler step
             if self.rtc_processor is not None and self.rtc_processor.is_debug_enabled():
                 self.rtc_processor.track(time=time, x_t=x_t, v_t=v_t)
 
+            time += dt
+
         return x_t
 
     def denoise_step(

src/lerobot/policies/pi05/modeling_pi05.py

@@ -538,8 +538,6 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         if config.compile_model:
             torch.set_float32_matmul_precision("high")
             self.sample_actions = torch.compile(self.sample_actions, mode=config.compile_mode)
-            # Also compile the main forward pass used during training
-            self.forward = torch.compile(self.forward, mode=config.compile_mode)
 
         msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
 
@@ -787,13 +785,16 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         )
 
         dt = -1.0 / num_steps
+        dt = torch.tensor(dt, dtype=torch.float32, device=device)
 
         x_t = noise
-        for step in range(num_steps):
-            time = 1.0 + step * dt
-            time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)
+        time = torch.tensor(1.0, dtype=torch.float32, device=device)
+        while time >= -dt / 2:
+            expanded_time = time.expand(bsize)
 
-            def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
+            # Define a closure function to properly capture expanded_time
+            # This avoids the lambda expression (E731) and loop variable binding (B023) issues
+            def denoise_step_partial_call(input_x_t, current_timestep=expanded_time):
                 return self.denoise_step(
                     prefix_pad_masks=prefix_pad_masks,
                     past_key_values=past_key_values,
@@ -817,11 +818,15 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             else:
                 v_t = denoise_step_partial_call(x_t)
 
-            x_t = x_t + dt * v_t
+            # Euler step
+            x_t += dt * v_t
 
+            # Record x_t and v_t after Euler step
             if self.rtc_processor is not None and self.rtc_processor.is_debug_enabled():
                 self.rtc_processor.track(time=time, x_t=x_t, v_t=v_t)
 
+            time += dt
+
         return x_t
 
     def denoise_step(

src/lerobot/policies/sarm/__init__.py

@@ -14,7 +14,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .config_earthrover_mini_plus import EarthRoverMiniPlusConfig
-from .robot_earthrover_mini_plus import EarthRoverMiniPlus
+from lerobot.policies.sarm.configuration_sarm import SARMConfig
+from lerobot.policies.sarm.modeling_sarm import (
+    SARMRewardModel,
+    SARMTransformer,
+)
+from lerobot.policies.sarm.processor_sarm import (
+    SARMEncodingProcessorStep,
+    make_sarm_pre_post_processors,
+)
+
+__all__ = [
+    "SARMConfig",
+    "SARMRewardModel",
+    "SARMTransformer",
+    "SARMEncodingProcessorStep",
+    "make_sarm_pre_post_processors",
+]
 
-__all__ = ["EarthRoverMiniPlus", "EarthRoverMiniPlusConfig"]

src/lerobot/policies/sarm/configuration_sarm.py

@@ -0,0 +1,186 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import PolicyFeature, FeatureType, NormalizationMode
+from lerobot.optim.optimizers import AdamWConfig
+from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
+
+
+@PreTrainedConfig.register_subclass("sarm")
+@dataclass
+class SARMConfig(PreTrainedConfig):
+    """Configuration class for SARM (Stage-Aware Reward Modeling)"""
+    
+    # CLIP params
+    image_dim: int = 512 
+    text_dim: int = 512
+    num_frames: int = 9  # 1 initial + 8 consecutive frames
+    frame_gap: int = 30  # Frame gap between frames (at 30 fps = 1 second)
+    
+    # Architecture params
+    hidden_dim: int = 768  
+    num_heads: int = 12  
+    num_layers: int = 8  
+    max_state_dim: int = 32
+    num_stages: int = 5  # Number of task stages (auto-updated from annotations if available)
+    subtask_names: list | None = None  # List of subtask names (auto-populated from annotations)
+    temporal_proportions: list | None = None  # Temporal proportions for each stage (auto-computed from annotations)
+    max_length: int = num_frames  # Maximum video sequence length (matches num_frames)
+    use_temporal_sampler: bool = True  # Always enable temporal sequence loading
+    
+    # Training params
+    batch_size: int = 64
+    clip_batch_size: int = 64  # Batch size for CLIP encoding
+    dropout: float = 0.1
+    stage_loss_weight: float = 1.0  # Weight for stage classification loss when using subtask annotations
+    
+    pretrained_model_path: str | None = None
+    device: str | None = None
+    
+    # Processor settings
+    image_key: str = "observation.images.top"  # Key for image used from the dataset
+
+    # State key in the dataset (for normalization)
+    state_key: str = "observation.state"
+    
+    # Populated by the processor (video_features, state_features, text_features)
+    input_features: dict = field(default_factory=lambda: {})
+    
+    # Output features
+    output_features: dict = field(default_factory=lambda: {
+        "stage": PolicyFeature(shape=(9, 5), type=FeatureType.REWARD),
+        "progress": PolicyFeature(shape=(9, 1), type=FeatureType.REWARD),
+    })
+
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,
+            "LANGUAGE": NormalizationMode.IDENTITY,
+            "REWARD": NormalizationMode.IDENTITY,
+        }
+    )
+    
+    def __post_init__(self):
+        super().__post_init__()
+
+        # Add the image_key as VISUAL
+        if self.image_key:
+            self.input_features[self.image_key] = PolicyFeature(
+                shape=(480, 640, 3),
+                type=FeatureType.VISUAL
+            )
+        
+        # Add state_key as STATE
+        self.input_features[self.state_key] = PolicyFeature(
+            shape=(self.max_state_dim,),  # Single frame state, temporal sampling handles sequence
+            type=FeatureType.STATE
+        )
+        
+        # Update output features with actual dimensions
+        self.output_features["stage"] = PolicyFeature(
+            shape=(self.num_frames, self.num_stages), 
+            type=FeatureType.REWARD
+        )
+        self.output_features["progress"] = PolicyFeature(
+            shape=(self.num_frames, 1), 
+            type=FeatureType.REWARD
+        )
+        
+        # Validate configuration
+        if self.hidden_dim % self.num_heads != 0:
+            raise ValueError(
+                f"hidden_dim ({self.hidden_dim}) must be divisible by num_heads ({self.num_heads})"
+            )
+        
+        if self.max_length != self.num_frames:
+            raise ValueError(
+                f"max_length ({self.max_length}) must equal num_frames ({self.num_frames})"
+            )
+        
+        if self.num_stages < 2:
+            raise ValueError(f"num_stages must be at least 2, got {self.num_stages}")
+    
+    def get_optimizer_preset(self) -> AdamWConfig:
+        """Get default optimizer configuration for SARM training."""
+        return AdamWConfig(
+            lr=5e-5,
+            weight_decay=1e-3,
+            betas=(0.9, 0.999),
+            eps=1e-8,
+        )
+    
+    def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
+        """Get default learning rate scheduler configuration."""
+        return CosineDecayWithWarmupSchedulerConfig(
+            peak_lr=5e-5,
+            decay_lr=5e-6,
+            num_warmup_steps=500,
+            num_decay_steps=50000,
+        )
+    
+    def validate_features(self) -> None:
+        """Validate input and output features."""
+        pass
+    
+    @property
+    def observation_delta_indices(self) -> list[int]:
+        """Load frames for SARM temporal sampling with SYMMETRIC/BIDIRECTIONAL pattern.
+        
+        The model uses 9 frames with symmetric context around current frame:
+        - Frame 0: Initial frame of the episode (clamped via large negative delta)
+        - Frames 1-8: Symmetric context: 4 before + current + 3 after
+        
+        Pattern: [initial, t-4*gap, t-3*gap, t-2*gap, t-gap, t, t+gap, t+2*gap, t+3*gap]
+        
+        Boundary handling (done by dataset loader):
+        - Early frames: backward indices clamp to 0 (first frame)
+        - Late frames: forward indices clamp to episode end (last frame)
+        
+        This enables truly uniform sampling across entire episodes.
+        
+        Returns:
+            9 delta indices: [-1_000_000, -4*gap, -3*gap, -2*gap, -gap, 0, gap, 2*gap, 3*gap]
+        """
+        initial_frame_delta = -1_000_000
+        
+        # Symmetric pattern: 4 frames before, current (0), 3 frames after = 8 context frames
+        symmetric_deltas = [
+            -4 * self.frame_gap,
+            -3 * self.frame_gap,
+            -2 * self.frame_gap,
+            -1 * self.frame_gap,
+            0,  # current frame
+            1 * self.frame_gap,
+            2 * self.frame_gap,
+            3 * self.frame_gap,
+        ]
+        
+        return [initial_frame_delta] + symmetric_deltas
+    
+    @property
+    def action_delta_indices(self) -> None:
+        """SARM is a reward model, not an action policy."""
+        return None
+    
+    @property
+    def reward_delta_indices(self) -> None:
+        """SARM doesn't use delta rewards."""
+        return None
+

src/lerobot/policies/sarm/modeling_sarm.py

@@ -0,0 +1,650 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+from typing import List, Union, Optional
+import random
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from transformers import CLIPModel, CLIPProcessor
+from torch import Tensor
+
+from lerobot.policies.sarm.configuration_sarm import SARMConfig
+from lerobot.policies.sarm.sarm_utils import compute_cumulative_progress_batch, pad_state_to_max_dim
+from lerobot.policies.pretrained import PreTrainedPolicy
+
+class SARMTransformer(nn.Module):
+    """
+    SARM Transformer model for stage-aware reward prediction.
+    
+    This model has a dual-head architecture:
+    1. Stage estimator: Predicts the high-level task stage (classification)
+    2. Subtask estimator: Predicts fine-grained progress within the stage (regression)
+    """
+    
+    def __init__(
+        self,
+        video_dim: int = 512,  
+        text_dim: int = 512, 
+        max_state_dim: int = 32, 
+        hidden_dim: int = 768,
+        num_heads: int = 12,
+        num_layers: int = 8,
+        num_stages: int = 5,
+        max_length: int = 9,
+        dropout: float = 0.1,
+        temporal_proportions: list[float] | None = None
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_dim
+        self.max_length = max_length
+        self.num_stages = num_stages
+        self.max_state_dim = max_state_dim
+        
+        if temporal_proportions is None:
+            raise ValueError(
+                "temporal_proportions is required for SARM. "
+                "Provide subtask annotations in your dataset or set temporal_proportions in config."
+            )
+        
+        # ᾱ_k: proportion for each stage
+        alpha = torch.tensor(temporal_proportions, dtype=torch.float32)
+        
+        # P_k: cumulative proportion up to stage k (P_0 = 0)
+        cumulative = torch.zeros(num_stages + 1, dtype=torch.float32)
+        cumulative[1:] = torch.cumsum(alpha, dim=0)
+        self.register_buffer('alpha', alpha)
+        self.register_buffer('cumulative_prior', cumulative)
+        
+        self.video_proj = nn.Linear(video_dim, hidden_dim)
+        self.text_proj = nn.Linear(text_dim, hidden_dim)
+        self.state_proj = nn.Linear(max_state_dim, hidden_dim) 
+        
+        # Position embedding only for the first frame
+        self.first_pos_embed = nn.Parameter(torch.randn(1, hidden_dim))
+        
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=hidden_dim,
+            nhead=num_heads,
+            dim_feedforward=hidden_dim * 4,
+            dropout=dropout,
+            batch_first=True
+        )
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        
+        # Stage estimator head (classification)
+        self.stage_head = nn.Sequential(
+            nn.Linear(hidden_dim, 512),
+            nn.LayerNorm(512),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(512, num_stages)
+        )
+        
+        # Subtask estimator head (regression)
+        self.stage_embedding = nn.Embedding(num_stages, hidden_dim // 4)
+        subtask_input_dim = hidden_dim + hidden_dim // 4
+        self.subtask_head = nn.Sequential(
+            nn.Linear(subtask_input_dim, 512),
+            nn.LayerNorm(512),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(512, 1),
+            nn.Sigmoid()
+        )
+        
+        # Attention mask
+        self.register_buffer("attention_mask", None, persistent=False)
+    
+    def _get_attention_mask(self, seq_length: int, device: torch.device) -> torch.Tensor:
+        """Generate or retrieve cached causal attention mask."""
+        if self.attention_mask is None or self.attention_mask.shape[0] != seq_length:
+            # Create causal mask
+            mask = nn.Transformer.generate_square_subsequent_mask(seq_length, device=device)
+            self.attention_mask = mask
+        return self.attention_mask
+    
+    def forward(
+        self, 
+        video_frames: torch.Tensor, 
+        text_embed: torch.Tensor,
+        state_features: Optional[torch.Tensor] = None
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Forward pass through the SARM transformer.
+        
+        Args:
+            video_frames: Video frame embeddings (batch_size, seq_len, video_dim)
+            text_embed: Text embeddings (batch_size, text_dim)
+            state_features: Joint state features (batch_size, seq_len, state_dim)
+            
+        Returns:
+            Tuple of:
+                - Stage logits for each frame (batch_size, seq_len, num_stages)
+                - Stage probabilities (batch_size, seq_len, num_stages)
+                - Progress predictions for each frame (batch_size, seq_len, 1)
+        """        
+        # Project inputs to common dimension
+        video_embed = self.video_proj(video_frames)  # [batch_size, seq_len, hidden_dim]
+        text_embed = self.text_proj(text_embed).unsqueeze(1)  # [batch_size, 1, hidden_dim]
+
+        # Pad state features to max_state_dim before projection
+        state_features_padded = pad_state_to_max_dim(state_features, self.max_state_dim)
+
+        state_embed = self.state_proj(state_features_padded)  # [batch_size, seq_len, hidden_dim]
+
+        # Fuse video and state features 
+        video_embed = video_embed + state_embed
+        
+        # Add positional embedding to first video frame
+        video_embed[:, 0] += self.first_pos_embed
+        
+        # Combine sequence: [text, video_frames]
+        sequence = torch.cat([text_embed, video_embed], dim=1)
+        
+        # Get causal attention mask
+        seq_length = sequence.shape[1]
+        attention_mask = self._get_attention_mask(seq_length, sequence.device)
+        
+        # Pass through transformer with causal masking
+        transformed = self.transformer(sequence, mask=attention_mask, is_causal=True)
+        
+        # Get frame features
+        frame_features = transformed[:, 1:]  # [batch_size, seq_len, hidden_dim]
+        
+        # Stage estimation
+        stage_logits = self.stage_head(frame_features)  # [batch_size, seq_len, num_stages]
+        stage_probs = F.softmax(stage_logits, dim=-1)  # [batch_size, seq_len, num_stages]
+        
+        # Get predicted stage indices
+        stage_indices = torch.argmax(stage_probs, dim=-1)  # [batch_size, seq_len]
+        
+        # Get stage embeddings for conditioning
+        stage_embeds = self.stage_embedding(stage_indices) 
+        
+        # Concatenate frame features with stage embeddings
+        conditioned_features = torch.cat([frame_features, stage_embeds], dim=-1)
+        
+        # Subtask progress estimation (conditioned on stage)
+        # τ̂ = within-subtask progress (0-1)
+        tau_preds = self.subtask_head(conditioned_features)  # [batch_size, seq_len, 1]
+        
+        # Convert τ̂ to cumulative progress ŷ using Paper Formula (2):
+        # ŷ = P_{k-1} + ᾱ_k × τ̂
+        progress_preds = compute_cumulative_progress_batch(
+            tau_preds, stage_indices, self.alpha, self.cumulative_prior
+        )
+        
+        return stage_logits, stage_probs, progress_preds
+
+
+class SARMRewardModel(PreTrainedPolicy):
+    """
+    SARM Reward Model for stage-aware task completion rewards.
+    
+    Per SARM paper (Appendix A.4): "We employ a frozen clip-vit-base-patch32 encoder 
+    to process both RGB image sequences and task descriptions."
+    
+    This model combines:
+    - CLIP for encoding video frames AND text descriptions
+    - SARMTransformer for predicting task stage and progress
+    - Optional RA-BC (Reward-Aligned Behavior Cloning) for weighted training
+    """
+    
+    name = "sarm"
+    config_class = SARMConfig
+    
+    def __init__(self, config: SARMConfig, dataset_stats: dict | None = None, dataset_meta=None):
+        super().__init__(config, dataset_stats)
+        config.validate_features() 
+        self.config = config
+        self.dataset_stats = dataset_stats
+        self.device = torch.device(config.device if config.device else "cuda" if torch.cuda.is_available() else "cpu")
+        
+        # Load temporal proportions from dataset
+        if config.temporal_proportions is None and dataset_meta is not None:
+            self._load_temporal_proportions(dataset_meta)
+        
+        logging.info("Loading CLIP encoder")
+        self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+        self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32", use_fast=True)
+        self.clip_model.to(self.device)
+        self.clip_model.eval()
+        
+        self.sarm_transformer = SARMTransformer(
+            video_dim=config.image_dim,
+            text_dim=config.text_dim,
+            max_state_dim=config.max_state_dim,
+            hidden_dim=config.hidden_dim,
+            num_heads=config.num_heads,
+            num_layers=config.num_layers,
+            num_stages=config.num_stages,
+            max_length=config.max_length,
+            dropout=config.dropout,
+            temporal_proportions=config.temporal_proportions
+        )
+        self.sarm_transformer.to(self.device)
+        logging.info(f"SARM initialized on {self.device}")
+    
+    def _load_temporal_proportions(self, dataset_meta) -> None:
+        """
+        Load pre-computed temporal proportions from dataset metadata JSON file.
+
+        The temporal proportions are computed during dataset annotation using SARM Paper Formula (1):
+            ᾱ_k = (1/M) × Σ_i (L_{i,k} / T_i)
+        """
+        import json
+        
+        proportions_path = dataset_meta.root / "meta" / "temporal_proportions.json"
+        
+        if not proportions_path.exists():
+            raise ValueError(
+                f"Temporal proportions not found at {proportions_path}. "
+                "Run the subtask annotation tool first to compute and save temporal proportions."
+            )
+        
+        with open(proportions_path, "r") as f:
+            temporal_proportions_dict = json.load(f)
+        
+        # Sort subtask names for consistent ordering
+        subtask_names = sorted(temporal_proportions_dict.keys())
+        
+        self.config.num_stages = len(subtask_names)
+        self.config.subtask_names = subtask_names
+        self.config.temporal_proportions = [temporal_proportions_dict[name] for name in subtask_names]
+        
+        logging.info(f"Loaded {len(subtask_names)} subtasks: {subtask_names}")
+        logging.info(f"Temporal proportions: {temporal_proportions_dict}")
+    
+    def to(self, device):
+        """Override to method to ensure all components move together."""
+        super().to(device)
+        self.device = device if isinstance(device, torch.device) else torch.device(device)
+        self.clip_model.to(device)
+        self.sarm_transformer.to(device)
+        return self
+    
+    @torch.no_grad()
+    def encode_images(self, images: np.ndarray) -> np.ndarray:
+        """
+        Encode video frames using CLIP.
+        
+        Args:
+            images: Video frames with shape (num_videos, num_frames, H, W, C) in uint8.
+                   Can also be (num_frames, H, W, C) for a single video.
+                   
+        Returns:
+            Encoded image features (num_videos, num_frames, 512) or (num_frames, 512).
+        """
+        # Handle single video case
+        single_video = False
+        if len(images.shape) == 4:
+            images = images[np.newaxis, ...]
+            single_video = True
+        
+        assert len(images.shape) == 5, f"Expected 5D input (num_videos, num_frames, H, W, C), got {images.shape}"
+        
+        all_embeddings = []
+        
+        for video in images:
+            video_embeddings = []
+            
+            # Convert frames to PIL images for CLIP processor
+            frames = []
+            for frame in video:
+                if frame.shape[0] == 3:  # Channel first
+                    frame = frame.transpose(1, 2, 0)
+                if frame.dtype != np.uint8:
+                    frame = (frame * 255).astype(np.uint8) if frame.max() <= 1.0 else frame.astype(np.uint8)
+                frames.append(Image.fromarray(frame))
+            
+            # Batch process frames with CLIP
+            for i in range(0, len(frames), self.config.clip_batch_size):
+                batch = frames[i:i + self.config.clip_batch_size]
+                inputs = self.clip_processor(images=batch, return_tensors="pt")
+                inputs = {k: v.to(self.device) for k, v in inputs.items()}
+                
+                # Get image embeddings from CLIP
+                embeddings = self.clip_model.get_image_features(**inputs).detach().cpu()
+                
+                # Handle single frame case
+                if embeddings.dim() == 1:
+                    embeddings = embeddings.unsqueeze(0)
+                
+                video_embeddings.append(embeddings)
+            
+            video_embeddings = torch.cat(video_embeddings)
+            all_embeddings.append(video_embeddings)
+        
+        result = torch.stack(all_embeddings).numpy()
+        
+        if single_video:
+            result = result[0]
+        
+        return result
+    
+    @torch.no_grad()
+    def encode_text(self, text: Union[str, List[str]]) -> np.ndarray:
+        """
+        Encode text using CLIP text encoder (per SARM paper A.4).
+        
+        Args:
+            text: Text string or list of text strings.
+            
+        Returns:
+            Encoded text features (batch_size, 512) or (512,) for single text.
+        """
+        if isinstance(text, str):
+            text = [text]
+            single_text = True
+        else:
+            single_text = False
+        
+        # Use CLIP's tokenizer directly (avoids image processor validation issues)
+        tokenizer = self.clip_processor.tokenizer
+        
+        # Process in batches
+        all_embeddings = []
+        for i in range(0, len(text), self.config.batch_size):
+            batch_text = text[i:i + self.config.batch_size]
+            
+            inputs = tokenizer(batch_text, return_tensors="pt", padding=True, truncation=True)
+            inputs = {k: v.to(self.device) for k, v in inputs.items()}
+            
+            text_embeddings = self.clip_model.get_text_features(**inputs)
+            all_embeddings.append(text_embeddings.cpu())
+        
+        result = torch.cat(all_embeddings).numpy()
+        
+        if single_text:
+            result = result[0]
+        
+        return result
+    
+    @torch.no_grad()
+    def calculate_rewards(
+        self,
+        text_embeddings: Union[np.ndarray, torch.Tensor],
+        video_embeddings: Union[np.ndarray, torch.Tensor],
+        state_features: Optional[Union[np.ndarray, torch.Tensor]] = None,
+        return_all_frames: bool = False,
+        return_stages: bool = False
+    ) -> Union[np.ndarray, tuple]:
+        """
+        Calculate rewards for given text, video, and state representations.
+        
+        Args:
+            text_embeddings: Encoded text representations (batch_size, 512)
+            video_embeddings: Encoded video representations (batch_size, num_frames, 512)
+            state_features: Joint state features (batch_size, num_frames, state_dim)
+            return_all_frames: If True, return rewards for all frames
+            return_stages: If True, also return stage predictions
+            
+        Returns:
+            If return_stages=False:
+                Reward values (batch_size,) or (batch_size, num_frames)
+            If return_stages=True:
+                Tuple of (rewards, stage_probs)
+        """
+        if isinstance(text_embeddings, np.ndarray):
+            text_embeddings = torch.tensor(text_embeddings, dtype=torch.float32)
+        if isinstance(video_embeddings, np.ndarray):
+            video_embeddings = torch.tensor(video_embeddings, dtype=torch.float32)
+        if state_features is not None and isinstance(state_features, np.ndarray):
+            state_features = torch.tensor(state_features, dtype=torch.float32)
+        
+        # Handle single sample case
+        if text_embeddings.dim() == 1:
+            text_embeddings = text_embeddings.unsqueeze(0)
+            video_embeddings = video_embeddings.unsqueeze(0)
+            if state_features is not None:
+                state_features = state_features.unsqueeze(0)
+            single_sample = True
+        else:
+            single_sample = False
+        
+        # Process in batches
+        all_rewards = []
+        all_stage_probs = []
+        
+        for i in range(0, len(video_embeddings), self.config.batch_size):
+            batch_texts = text_embeddings[i:i + self.config.batch_size].to(self.device)
+            batch_videos = video_embeddings[i:i + self.config.batch_size].to(self.device)
+            batch_states = None
+            if state_features is not None:
+                batch_states = state_features[i:i + self.config.batch_size].to(self.device)
+            
+            # Get predictions
+            stage_logits, stage_probs, progress_preds = self.sarm_transformer(
+                batch_videos.float(), batch_texts.float(), batch_states.float() if batch_states is not None else None
+            )
+            
+            if return_all_frames:
+                all_rewards.append(progress_preds.squeeze(-1).cpu())
+            else:
+                # Return only last frame reward
+                all_rewards.append(progress_preds[:, -1, 0].cpu())
+            
+            if return_stages:
+                all_stage_probs.append(stage_probs.cpu())
+        
+        rewards = torch.cat(all_rewards).numpy()
+        
+        if single_sample:
+            rewards = rewards[0] if not return_all_frames else rewards[0]
+        
+        if return_stages:
+            stage_probs = torch.cat(all_stage_probs).numpy()
+            if single_sample:
+                stage_probs = stage_probs[0]
+            return rewards, stage_probs
+        
+        return rewards
+    
+    def train(self, mode: bool = True):
+        """Overwrite train method to ensure CLIP encoder stays frozen during training"""
+        super().train(mode)
+        self.clip_model.eval()
+        self.sarm_transformer.train(mode)
+        return self
+    
+    def eval(self):
+        """Overwrite eval method to ensure CLIP encoder stays frozen during evaluation"""
+        return self.train(False)
+    
+    def parameters(self):
+        """Override to return trainable parameters (only SARM transformer, not CLIP encoder)."""
+        return self.sarm_transformer.parameters()
+    
+    def get_optim_params(self):
+        """Override to return optimizer parameters (only SARM transformer, not CLIP encoder)."""
+        return self.parameters()
+    
+    def reset(self):
+        """Required by PreTrainedPolicy but not used for reward models."""
+        pass
+    
+    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+        """Required by PreTrainedPolicy but not used for reward models."""
+        raise NotImplementedError("SARM model does not predict action chunks")
+    
+    def select_action(self, batch: dict[str, Tensor]) -> Tensor:
+        """Required by PreTrainedPolicy but not used for SARM."""
+        raise NotImplementedError("SARM model does not select actions")
+    
+    def _apply_temporal_augmentation(
+        self, 
+        video: torch.Tensor, 
+        progress: torch.Tensor, 
+        state: torch.Tensor | None,
+        max_length: int,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
+        """Apply temporal augmentation by appending reversed frames (SARM paper A.4).
+        
+        This helps the model learn to handle non-monotonic progress (failures, recoveries).
+        Appends 1-4 reversed frames to simulate going backwards in task progress.
+        """
+        num_reverse = random.randint(1, min(4, max_length - 1))
+        
+        # Reverse and take frames (skip first which is last of original)
+        reversed_video = video.flip(0)[1:num_reverse + 1]
+        reversed_progress = progress.flip(0)[1:num_reverse + 1]
+        
+        # Concatenate and trim
+        video = torch.cat([video, reversed_video], dim=0)[:max_length]
+        progress = torch.cat([progress, reversed_progress], dim=0)[:max_length]
+        
+        if state is not None:
+            reversed_state = state.flip(0)[1:num_reverse + 1]
+            state = torch.cat([state, reversed_state], dim=0)[:max_length]
+        
+        return video, progress, state
+    
+    def _ensure_sequence_length(self, tensor: torch.Tensor, target_len: int) -> torch.Tensor:
+        """Pad or trim tensor to target length."""
+        current_len = tensor.shape[0]
+        if current_len == target_len:
+            return tensor
+        if current_len < target_len:
+            padding = target_len - current_len
+            return torch.cat([tensor, tensor[-1:].expand(padding, *tensor.shape[1:])])
+        return tensor[:target_len]
+    
+    def forward(self, batch):
+        """
+        Forward pass for SARM reward model training.
+        
+        Uses annotation-based progress targets following SARM paper Eq. 2:
+        yt = Pk-1 + α̅k × τt
+        where:
+        - τt = (t - sk) / (ek - sk) is within-subtask normalized time
+        - Pk-1 is cumulative prior (sum of previous subtask proportions)
+        - α̅k is the temporal proportion for subtask k
+        
+        Args:
+            batch: Dictionary with 'observation' containing:
+                - 'video_features': (B, T, 512) pre-encoded video features
+                - 'text_features': (B, 512) pre-encoded text features (CLIP)
+                - 'state_features': (B, T, state_dim) joint state features
+                - 'stage_labels': (B, T) stage labels from annotations
+                - 'progress_targets': (B, T, 1) progress targets from annotations
+        
+        Returns:
+            Tuple of (total_loss, output_dict with loss components)
+        """
+        observation = batch.get('observation', batch)
+        
+        # Extract required features
+        video_features = observation['video_features'].to(self.device)
+        text_features = observation['text_features'].to(self.device)
+        state_features = observation.get('state_features').to(self.device)
+        
+        batch_size = video_features.shape[0]
+        max_length = self.config.num_frames
+        
+        # Ensure 3D video features (B, T, D)
+        if video_features.dim() == 2:
+            video_features = video_features.unsqueeze(1).expand(-1, max_length, -1)
+        if state_features is not None and state_features.dim() == 2:
+            state_features = state_features.unsqueeze(1).expand(-1, max_length, -1)
+        
+        # Get annotation-based progress targets (required for SARM paper formula)
+        progress_from_annotations = observation.get('progress_targets')
+        if progress_from_annotations is None:
+            raise ValueError("progress_targets from annotations is required for SARM training")
+        
+        progress_from_annotations = progress_from_annotations.to(self.device)
+        if progress_from_annotations.dim() == 2:
+            progress_from_annotations = progress_from_annotations.unsqueeze(-1)
+        if progress_from_annotations.dim() == 3 and progress_from_annotations.shape[0] == 1:
+            progress_from_annotations = progress_from_annotations.expand(batch_size, -1, -1)
+        
+        # Process each sample: apply temporal REWIND augmentation 
+        processed_videos = []
+        processed_states = []
+        progress_targets = []
+        
+        for i in range(batch_size):
+            video = video_features[i]
+            state = state_features[i] if state_features is not None else None
+            progress = progress_from_annotations[i].squeeze(-1)  # (T,)
+            
+            # Apply temporal REWIND augmentation with 50% probability: appends up to 4 reversed frames to simulate failures/recoveries
+            if random.random() < 0.5:
+                video, progress, state = self._apply_temporal_augmentation(video, progress, state, max_length)
+            
+            # Ensure correct sequence length
+            video = self._ensure_sequence_length(video, max_length)
+            progress = self._ensure_sequence_length(progress.unsqueeze(-1), max_length).squeeze(-1)
+            if state is not None:
+                state = self._ensure_sequence_length(state, max_length)
+            
+            processed_videos.append(video)
+            progress_targets.append(progress)
+            if state is not None:
+                processed_states.append(state)
+        
+        # Stack into batches
+        processed_videos = torch.stack(processed_videos)
+        progress_targets = torch.stack(progress_targets).unsqueeze(-1)  # (B, T, 1)
+        processed_states = torch.stack(processed_states) if processed_states else None
+        
+        # Get model predictions
+        stage_logits, stage_probs, progress_preds = self.sarm_transformer(
+            processed_videos, text_features, processed_states
+        )
+        
+        # Compute progress loss (MSE)
+        progress_loss = F.mse_loss(progress_preds, progress_targets)
+        output_dict = {'progress_loss': progress_loss.item()}
+        total_loss = progress_loss
+        
+        # Compute stage loss (cross-entropy)
+        stage_labels = observation.get('stage_labels')
+        if stage_labels is None:
+            raise ValueError("stage_labels from annotations is required for SARM training")
+        
+        stage_labels = stage_labels.to(self.device)
+        if stage_labels.dim() == 1:
+            stage_labels = stage_labels.unsqueeze(0).expand(batch_size, -1)
+        stage_loss = compute_stage_loss(stage_logits, stage_labels)
+        total_loss = total_loss + self.config.stage_loss_weight * stage_loss
+        output_dict['stage_loss'] = stage_loss.item()
+        
+        # Misaligned loss: 20% probability
+        if random.random() < 0.2:
+            shuffle_idx = torch.randperm(batch_size, device=self.device)
+            _, _, misaligned_preds = self.sarm_transformer(
+                processed_videos, text_features[shuffle_idx], processed_states
+            )
+            misaligned_loss = F.mse_loss(misaligned_preds, torch.zeros_like(misaligned_preds))
+            total_loss = total_loss + misaligned_loss
+            output_dict['misaligned_loss'] = misaligned_loss.item()
+        
+        output_dict['total_loss'] = total_loss.item()
+        return total_loss, output_dict
+
+def compute_stage_loss(stage_logits: torch.Tensor, target_stages: torch.Tensor) -> torch.Tensor:
+    _, _, num_stages = stage_logits.shape
+    stage_logits_flat = stage_logits.reshape(-1, num_stages)
+    target_stages_flat = target_stages.reshape(-1)
+    
+    loss = F.cross_entropy(stage_logits_flat, target_stages_flat)
+    return loss

src/lerobot/policies/sarm/processor_sarm.py

@@ -0,0 +1,644 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any
+import numpy as np
+import torch
+from PIL import Image
+import pandas as pd
+from transformers import CLIPModel, CLIPProcessor
+
+from lerobot.processor.core import TransitionKey
+from lerobot.policies.sarm.configuration_sarm import SARMConfig
+from lerobot.policies.sarm.sarm_utils import compute_tau, compute_cumulative_progress_batch, pad_state_to_max_dim
+from lerobot.processor import (
+    ProcessorStep,
+    PolicyProcessorPipeline,
+    PolicyAction,
+    DeviceProcessorStep,
+    AddBatchDimensionProcessorStep,
+    NormalizerProcessorStep,
+)
+from lerobot.processor.converters import (
+    policy_action_to_transition,
+    transition_to_policy_action,
+    from_tensor_to_numpy,
+)
+from lerobot.processor.pipeline import PipelineFeatureType
+from lerobot.processor.core import EnvTransition, TransitionKey
+from lerobot.configs.types import PolicyFeature, FeatureType
+from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
+
+
+class SARMEncodingProcessorStep(ProcessorStep):
+    """ProcessorStep that encodes images and text with CLIP."""
+    def __init__(
+        self,
+        config: SARMConfig,
+        image_key: str | None = None,
+        dataset_meta = None,
+        dataset_stats: dict | None = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.image_key = image_key or config.image_key
+        self.dataset_meta = dataset_meta
+        self.dataset_stats = dataset_stats
+        self.temporal_proportions = {name: prop for name, prop in zip(self.config.subtask_names, self.config.temporal_proportions)}
+        self.subtask_names = self.config.subtask_names
+
+        self.device = torch.device(
+            self.config.device if self.config.device 
+            else "cuda" if torch.cuda.is_available() else "cpu"
+        )
+        
+        self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+        self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32", use_fast=True)
+        self.clip_model.to(self.device)
+        self.clip_model.eval()
+
+    def _find_episode_for_frame(self, frame_idx: int) -> int:
+        """Find the episode index for a given frame index."""
+        for ep_idx in range(len(self.dataset_meta.episodes)):
+            ep_start = self.dataset_meta.episodes[ep_idx]["dataset_from_index"]
+            ep_end = self.dataset_meta.episodes[ep_idx]["dataset_to_index"]
+            if ep_start <= frame_idx < ep_end:
+                return ep_idx
+        return 0  
+    
+    def _get_episode_indices(self, frame_indices: np.ndarray, episode_index) -> np.ndarray:
+        """Get episode indices for each frame index."""
+        if episode_index is None:
+            return np.array([self._find_episode_for_frame(int(f)) for f in frame_indices])
+        
+        episode_indices = np.atleast_1d(np.asarray(from_tensor_to_numpy(episode_index)))
+        
+        # If single episode but multiple frames, compute episode for each frame
+        if len(episode_indices) == 1 and len(frame_indices) > 1:
+            return np.array([self._find_episode_for_frame(int(f)) for f in frame_indices])
+        
+        return episode_indices
+    
+    def _compute_absolute_indices(self, frame_idx: int, ep_start: int, ep_end: int, num_frames: int) -> torch.Tensor:
+        """Compute absolute frame indices for symmetric bidirectional pattern.
+        
+        Pattern: [ep_start, t-4*gap, t-3*gap, t-2*gap, t-gap, t, t+gap, t+2*gap, t+3*gap]
+        
+        Boundary handling:
+        - Backward indices clamp to ep_start (first frame)
+        - Forward indices clamp to ep_end - 1 (last frame)
+        """
+        indices = []
+        indices.append(ep_start)  # Initial frame is always episode start
+        
+        # Symmetric context: 4 before, current, 3 after
+        num_before = 4
+        num_after = 3
+        last_valid_frame = ep_end - 1
+        
+        # Frames before current (clamp to first frame)
+        for i in range(num_before, 0, -1):
+            idx = max(ep_start, frame_idx - i * self.config.frame_gap)
+            indices.append(idx)
+        
+        # Current frame
+        indices.append(frame_idx)
+        
+        # Frames after current (clamp to last frame)
+        for i in range(1, num_after + 1):
+            idx = min(last_valid_frame, frame_idx + i * self.config.frame_gap)
+            indices.append(idx)
+        
+        return torch.tensor(indices)
+    
+    def _compute_episode_metadata(
+        self, 
+        frame_indices: np.ndarray, 
+        episode_indices: np.ndarray,
+        num_frames: int,
+    ) -> tuple[list | torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Compute episode metadata for all samples.
+        
+        Returns:
+            Tuple of (absolute_frame_indices, remaining_lengths, episode_lengths)
+        """
+        absolute_indices_list = []
+        remaining_lengths = []
+        episode_lengths = []
+        
+        for ep_idx, frame_idx in zip(episode_indices.tolist(), frame_indices.tolist()):
+            ep_idx, frame_idx = int(ep_idx), int(frame_idx)
+            ep_start = self.dataset_meta.episodes[ep_idx]["dataset_from_index"]
+            ep_end = self.dataset_meta.episodes[ep_idx]["dataset_to_index"]
+            
+            episode_lengths.append(ep_end - ep_start)
+            abs_indices = self._compute_absolute_indices(frame_idx, ep_start, ep_end, num_frames)
+            absolute_indices_list.append(abs_indices)
+            remaining_lengths.append(ep_end - abs_indices[0].item())
+        
+        return absolute_indices_list, torch.tensor(remaining_lengths), torch.tensor(episode_lengths)
+    
+    def _compute_stage_and_progress_for_frame(
+        self, 
+        current_frame: int,
+        subtask_names: list,
+        subtask_start_frames: list,
+        subtask_end_frames: list,
+        transition_smoothing_frames: int = 15,
+    ) -> tuple[int, float, dict[int, float] | None]:
+        """Compute stage index, cumulative progress, and soft stage labels for a single frame.
+        
+        Implements SARM Paper Formula (2):
+            y_t = P_{k-1} + ᾱ_k × τ_t
+        
+        where:
+            - τ_t = (t - s_k) / (e_k - s_k) is within-subtask progress
+            - P_{k-1} is cumulative prior (sum of previous subtask proportions)
+            - ᾱ_k is the temporal proportion for subtask k
+        
+        Additionally computes soft stage labels near transitions to mitigate discrete jumps
+        in the stage classifier. Near stage boundaries, labels are blended between adjacent
+        stages to encourage smoother predictions.
+        
+        Args:
+            current_frame: Frame index relative to episode start
+            subtask_names: List of subtask names for this episode
+            subtask_start_frames: List of subtask start frames
+            subtask_end_frames: List of subtask end frames
+            transition_smoothing_frames: Number of frames over which to smooth labels near transitions
+            
+        Returns:
+            Tuple of (stage_idx, cumulative_progress, soft_stage_labels)
+            - stage_idx: Hard stage index (for compatibility)
+            - cumulative_progress: Progress value in [0, 1]
+            - soft_stage_labels: Dict mapping stage_idx -> probability, or None if not near transition
+        """
+        # Get temporal proportions as list for compute_cumulative_progress
+        temporal_proportions_list = [
+            self.temporal_proportions.get(name, 0.0) for name in self.subtask_names
+        ]
+        num_stages = len(self.subtask_names)
+        
+        # Find which subtask this frame belongs to
+        for j, (name, start_frame, end_frame) in enumerate(zip(subtask_names, subtask_start_frames, subtask_end_frames)):
+            if current_frame >= start_frame and current_frame <= end_frame:
+                # Found the subtask, get its global index
+                stage_idx = self.subtask_names.index(name) if name in self.subtask_names else 0
+                
+                # Compute τ_t using utility function (Paper Formula 2)
+                tau = compute_tau(current_frame, start_frame, end_frame)
+                
+                # Compute cumulative progress using utility function (Paper Formula 2)
+                cumulative_progress = compute_cumulative_progress_batch(
+                    tau, stage_idx, temporal_proportions_list
+                )
+                
+                # Compute soft stage labels near transitions
+                soft_stage_labels = None
+                frames_from_start = current_frame - start_frame
+                frames_to_end = end_frame - current_frame
+                
+                if frames_from_start < transition_smoothing_frames and j > 0:
+                    # Near start of stage - blend with previous stage
+                    blend = frames_from_start / transition_smoothing_frames
+                    prev_name = subtask_names[j - 1]
+                    prev_stage_idx = self.subtask_names.index(prev_name) if prev_name in self.subtask_names else max(0, stage_idx - 1)
+                    soft_stage_labels = {prev_stage_idx: 1.0 - blend, stage_idx: blend}
+                    
+                elif frames_to_end < transition_smoothing_frames and j < len(subtask_names) - 1:
+                    # Near end of stage - blend with next stage
+                    blend = frames_to_end / transition_smoothing_frames
+                    next_name = subtask_names[j + 1]
+                    next_stage_idx = self.subtask_names.index(next_name) if next_name in self.subtask_names else min(num_stages - 1, stage_idx + 1)
+                    soft_stage_labels = {stage_idx: blend, next_stage_idx: 1.0 - blend}
+                
+                return stage_idx, cumulative_progress, soft_stage_labels
+        
+        # No matching subtask found
+        if current_frame < subtask_start_frames[0]:
+            return 0, 0.0, None
+        elif current_frame > subtask_end_frames[-1]:
+            return len(self.subtask_names) - 1, 1.0, None
+        else:
+            # Between subtasks - use previous subtask's end state (tau = 1.0)
+            for j in range(len(subtask_names) - 1):
+                if current_frame > subtask_end_frames[j] and current_frame < subtask_start_frames[j + 1]:
+                    name = subtask_names[j]
+                    stage_idx = self.subtask_names.index(name) if name in self.subtask_names else j
+                    
+                    # Completed subtask, so tau = 1.0
+                    cumulative_progress = compute_cumulative_progress_batch(
+                        1.0, stage_idx, temporal_proportions_list
+                    )
+                    return stage_idx, cumulative_progress, None
+        
+        return 0, 0.0, None
+    
+    def _compute_labels_for_sample(
+        self,
+        frame_idx: int,
+        ep_idx: int,
+        seq_len: int,
+        episodes_df: pd.DataFrame,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] | tuple[None, None, None]:
+        """Compute stage labels, progress targets, and soft stage labels for symmetric bidirectional pattern.
+        
+        Pattern: [initial, t-4*gap, t-3*gap, t-2*gap, t-gap, t, t+gap, t+2*gap, t+3*gap]
+        
+        Boundary handling:
+        - Before episode start: clamp to frame 0 (progress ~0%)
+        - After episode end: clamp to last frame (progress ~100%)
+        
+        Soft stage labels are computed near stage transitions to mitigate discrete jumps.
+        
+        Args:
+            frame_idx: The frame index for this sample
+            ep_idx: The episode index
+            seq_len: Number of frames in the sequence
+            episodes_df: DataFrame with episode metadata
+            
+        Returns:
+            Tuple of (stage_labels, progress_targets, soft_stage_labels):
+            - stage_labels: (T,) hard stage indices
+            - progress_targets: (T, 1) progress values
+            - soft_stage_labels: (T, num_stages) soft probability labels, or None if no transitions nearby
+        """
+        # Check if episode has valid annotations
+        if ep_idx >= len(episodes_df):
+            return None, None, None
+        
+        subtask_names = episodes_df.loc[ep_idx, 'subtask_names']
+        if subtask_names is None or (isinstance(subtask_names, float) and pd.isna(subtask_names)):
+            return None, None, None
+        
+        subtask_start_frames = episodes_df.loc[ep_idx, 'subtask_start_frames']
+        subtask_end_frames = episodes_df.loc[ep_idx, 'subtask_end_frames']
+        ep_start = self.dataset_meta.episodes[ep_idx]["dataset_from_index"]
+        ep_end = self.dataset_meta.episodes[ep_idx]["dataset_to_index"]
+        ep_length = ep_end - ep_start
+        last_valid_frame = ep_length - 1
+        
+        num_stages = len(self.subtask_names)
+        
+        # Generate labels for each frame in the sequence
+        stage_labels = []
+        progress_targets = []
+        soft_labels_list = []  # List of soft label dicts (or None)
+        has_any_soft_labels = False
+        
+        # Symmetric pattern: initial + 4 before + current + 3 after = 9 frames
+        num_before = 4
+        num_after = 3
+        
+        for i in range(seq_len):
+            if i == 0:
+                # Position 0: Initial frame of the episode
+                current_frame = 0  # Relative to episode start
+            elif i <= num_before:
+                # Positions 1-4: frames before current (with clamping to first frame)
+                offset = -(num_before - i + 1) * self.config.frame_gap
+                current_frame = max(0, frame_idx + offset - ep_start)
+            elif i == num_before + 1:
+                # Position 5: current frame
+                current_frame = frame_idx - ep_start
+            else:
+                # Positions 6-8: frames after current (with clamping to last frame)
+                offset = (i - num_before - 1) * self.config.frame_gap
+                current_frame = min(last_valid_frame, frame_idx + offset - ep_start)
+            
+            stage_idx, cumulative_progress, soft_stage_labels = self._compute_stage_and_progress_for_frame(
+                current_frame, subtask_names, subtask_start_frames, subtask_end_frames
+            )
+            
+            stage_labels.append(stage_idx)
+            progress_targets.append(cumulative_progress)
+            soft_labels_list.append(soft_stage_labels)
+            if soft_stage_labels is not None:
+                has_any_soft_labels = True
+        
+        stage_labels = torch.tensor(stage_labels, dtype=torch.long)
+        progress_targets = torch.tensor(progress_targets, dtype=torch.float32).unsqueeze(-1)
+        
+        # Convert soft labels to tensor if any exist
+        soft_stage_labels_tensor = None
+        if has_any_soft_labels:
+            soft_stage_labels_tensor = torch.zeros(seq_len, num_stages, dtype=torch.float32)
+            for i, soft_dict in enumerate(soft_labels_list):
+                if soft_dict is not None:
+                    for stage_idx, prob in soft_dict.items():
+                        soft_stage_labels_tensor[i, stage_idx] = prob
+                else:
+                    # Use hard one-hot label
+                    soft_stage_labels_tensor[i, stage_labels[i]] = 1.0
+        
+        return stage_labels, progress_targets, soft_stage_labels_tensor
+    
+    def _generate_stage_and_progress_labels(self, frame_index, episode_index, video_features):
+        """Generate stage labels, progress targets, and soft stage labels from subtask annotations.
+        
+        Args:
+            frame_index: Current frame index or tensor of indices
+            episode_index: Episode index or tensor of indices  
+            video_features: Video features tensor to determine sequence length
+            
+        Returns:
+            Tuple of (stage_labels, progress_targets, soft_stage_labels) or (None, None, None) if no annotations.
+            - stage_labels: (B, T) hard stage indices
+            - progress_targets: (B, T, 1) progress values
+            - soft_stage_labels: (B, T, num_stages) soft probability labels, or None
+        """
+        if self.temporal_proportions is None or episode_index is None:
+            return None, None, None
+        
+        # Normalize inputs to numpy arrays
+        frame_indices = np.atleast_1d(np.asarray(from_tensor_to_numpy(frame_index)))
+        episode_indices = self._get_episode_indices(frame_indices, episode_index)
+        
+        # Determine sequence length
+        if video_features is not None and video_features.dim() >= 2:
+            seq_len = video_features.shape[1]
+        else:
+            seq_len = 1
+        
+        episodes_df = self.dataset_meta.episodes.to_pandas()
+        num_stages = len(self.subtask_names)
+        
+        all_stage_labels = []
+        all_progress_targets = []
+        all_soft_stage_labels = []
+        has_any_soft_labels = False
+        
+        for ep_idx, frame_idx in zip(episode_indices.tolist(), frame_indices.tolist()):
+            stage_labels, progress_targets, soft_labels = self._compute_labels_for_sample(
+                int(frame_idx), int(ep_idx), seq_len, episodes_df
+            )
+            
+            if stage_labels is None:
+                all_stage_labels.append(torch.zeros(seq_len, dtype=torch.long))
+                all_progress_targets.append(torch.zeros(seq_len, 1, dtype=torch.float32))
+                all_soft_stage_labels.append(None)
+            else:
+                all_stage_labels.append(stage_labels)
+                all_progress_targets.append(progress_targets)
+                all_soft_stage_labels.append(soft_labels)
+                if soft_labels is not None:
+                    has_any_soft_labels = True
+        
+        stacked_stage_labels = torch.stack(all_stage_labels, dim=0)
+        stacked_progress_targets = torch.stack(all_progress_targets, dim=0)
+        
+        # Stack soft labels if any exist
+        stacked_soft_labels = None
+        if has_any_soft_labels:
+            soft_labels_tensors = []
+            for i, soft_labels in enumerate(all_soft_stage_labels):
+                if soft_labels is not None:
+                    soft_labels_tensors.append(soft_labels)
+                else:
+                    # Create one-hot from hard labels
+                    one_hot = torch.zeros(seq_len, num_stages, dtype=torch.float32)
+                    for t in range(seq_len):
+                        one_hot[t, all_stage_labels[i][t]] = 1.0
+                    soft_labels_tensors.append(one_hot)
+            stacked_soft_labels = torch.stack(soft_labels_tensors, dim=0)
+        
+        return stacked_stage_labels, stacked_progress_targets, stacked_soft_labels
+    
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        """Encode images, text, and normalize states in the transition."""
+
+        new_transition = transition.copy() if hasattr(transition, 'copy') else dict(transition)
+        observation = new_transition.get(TransitionKey.OBSERVATION)
+        
+        image = observation.get(self.image_key)
+        
+        if isinstance(image, torch.Tensor):
+            image = image.cpu().numpy()
+        video_features = self._encode_images_batch(image)
+        observation['video_features'] = video_features
+        
+        # Extract state and pad to max_state_dim (already normalized by NormalizerProcessorStep)
+        state_key = self.config.state_key
+        state_data = observation.get(state_key)
+        
+        if isinstance(state_data, torch.Tensor):
+            state_tensor = state_data.float()
+        else:
+            state_tensor = torch.tensor(state_data, dtype=torch.float32)
+        
+        observation['state_features'] = pad_state_to_max_dim(state_tensor, self.config.max_state_dim)
+        
+        comp_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
+        
+        # Get task description from dataset (complementary_data["task"])
+        task = comp_data.get('task')
+        if isinstance(task, list):
+            # If batch, take first task (assuming same task for all items in batch)
+            task = task[0] if task else ""
+        
+        # Encode text with CLIP
+        batch_size = video_features.shape[0]
+        observation['text_features'] = self._encode_text_clip(task, batch_size)
+        
+        frame_index = comp_data.get('index')
+        episode_index = comp_data.get('episode_index')
+        
+        if frame_index is None:
+            raise ValueError("Frame index ('index') not found in COMPLEMENTARY_DATA")
+        if episode_index is None:
+            raise ValueError("Episode index ('episode_index') not found in COMPLEMENTARY_DATA")
+        
+        # Compute episode metadata if dataset_meta is available
+        if self.dataset_meta is not None:
+            frame_indices = np.atleast_1d(np.asarray(from_tensor_to_numpy(frame_index)))
+            episode_indices = self._get_episode_indices(frame_indices, episode_index)
+            
+            # Determine number of frames from video features
+            if video_features.dim() >= 2:
+                num_frames = video_features.shape[1]
+            else:
+                num_frames = 1
+            
+            abs_indices, remaining, ep_lengths = self._compute_episode_metadata(
+                frame_indices, episode_indices, num_frames
+            )
+            observation['absolute_frame_indices'] = abs_indices
+            observation['remaining_length'] = remaining
+            observation['episode_length'] = ep_lengths
+        
+        # Generate stage labels, progress targets, and soft stage labels from subtask annotations
+        if self.temporal_proportions is not None and self.dataset_meta is not None:
+            stage_labels, progress_targets, soft_stage_labels = self._generate_stage_and_progress_labels(
+                frame_index, episode_index, video_features
+            )
+            if stage_labels is not None:
+                observation['stage_labels'] = stage_labels
+                observation['progress_targets'] = progress_targets
+                if soft_stage_labels is not None:
+                    observation['soft_stage_labels'] = soft_stage_labels
+        
+        new_transition[TransitionKey.OBSERVATION] = observation
+        return new_transition
+    
+    @torch.no_grad()
+    def _encode_images_batch(self, images: np.ndarray) -> torch.Tensor:
+        """Encode a batch of images using CLIP.
+        
+        Args:
+            images: Batched images with shape: (B, T, C, H, W)
+            
+        Returns:
+            Encoded feature vectors with shape (B, T, 512)
+        """
+
+        batch_size, seq_length = images.shape[0], images.shape[1] 
+        images = images.reshape(batch_size * seq_length, *images.shape[2:]) 
+        
+        # Convert to list of PIL images
+        num_frames = images.shape[0]
+        images_list = []
+        for i in range(num_frames):
+            img = images[i]
+            if img.shape[0] in [1, 3]:  # Channel first (C, H, W)
+                img = img.transpose(1, 2, 0)
+            
+            # Handle single channel
+            if img.shape[-1] == 1:
+                img = np.repeat(img, 3, axis=-1)
+            
+            # Convert to uint8
+            if img.dtype != np.uint8:
+                img = (img * 255).astype(np.uint8) if img.max() <= 1.0 else img.astype(np.uint8)
+            
+            images_list.append(Image.fromarray(img))
+        
+        # Encode each batch
+        all_embeddings = []
+        for i in range(0, num_frames, self.config.clip_batch_size):
+            batch_imgs = images_list[i:i + self.config.clip_batch_size]
+            
+            # Process with CLIP
+            inputs = self.clip_processor(images=batch_imgs, return_tensors="pt")
+            inputs = {k: v.to(self.device) for k, v in inputs.items()}
+            
+            # Get image embeddings
+            embeddings = self.clip_model.get_image_features(**inputs).detach().cpu()
+            
+            # Handle single frame case
+            if embeddings.dim() == 1:
+                embeddings = embeddings.unsqueeze(0)
+            
+            all_embeddings.append(embeddings)
+        
+        # Concatenate all embeddings
+        all_embeddings = torch.cat(all_embeddings)  # (B*T, 512)
+        
+        # Reshape back 
+        all_embeddings = all_embeddings.reshape(batch_size, seq_length, -1)  # (B, T, 512)
+        
+        return all_embeddings
+    
+    @torch.no_grad()
+    def _encode_text_clip(self, text: str, batch_size: int) -> torch.Tensor:
+        """Encode text using CLIP text encoder (per SARM paper A.4).
+        
+        Args:
+            text: Task description text to encode
+            batch_size: Batch size to replicate for
+            
+        Returns:
+            Encoded text features with shape (B, 512)
+        """
+        # Use CLIP's tokenizer directly for text
+        tokenizer = self.clip_processor.tokenizer
+        inputs = tokenizer([text], return_tensors="pt", padding=True, truncation=True)
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
+        
+        # Get text features from CLIP
+        text_embedding = self.clip_model.get_text_features(**inputs).detach().cpu()
+        
+        # Replicate for batch (B, 512)
+        text_embedding = text_embedding.expand(batch_size, -1)
+        
+        return text_embedding
+    
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        """Add encoded features to the observation features."""
+        features[PipelineFeatureType.OBSERVATION]['video_features'] = PolicyFeature(
+            type=FeatureType.VISUAL, 
+            shape=(self.config.num_frames, self.config.image_dim)
+        )
+        features[PipelineFeatureType.OBSERVATION]['text_features'] = PolicyFeature(
+            type=FeatureType.LANGUAGE, 
+            shape=(self.config.text_dim,)
+        )
+        features[PipelineFeatureType.OBSERVATION]['state_features'] = PolicyFeature(
+            type=FeatureType.STATE, 
+            shape=(self.config.num_frames, self.config.max_state_dim)
+        )
+        return features
+
+
+def make_sarm_pre_post_processors(
+    config: SARMConfig,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+    dataset_meta = None,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    """
+    Create pre-processor and post-processor pipelines for SARM.
+    
+    The pre-processing pipeline:
+    1. Adds batch dimension
+    2. Normalizes observation.state using NormalizerProcessorStep (MEAN_STD)
+    3. SARMEncodingProcessorStep:
+       - Encodes images with CLIP 
+       - Pads states to max_state_dim
+       - Encodes text with CLIP 
+    4. Moves data to device
+    
+    The post-processing pipeline:
+    1. Moves data to CPU
+    """
+    return (
+        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+            steps=[
+                    AddBatchDimensionProcessorStep(),
+                    NormalizerProcessorStep(
+                        features={**config.input_features, **config.output_features},
+                        norm_map=config.normalization_mapping,
+                        stats=dataset_stats,
+                    ),
+                    SARMEncodingProcessorStep(
+                        config=config,
+                        dataset_meta=dataset_meta,
+                        dataset_stats=dataset_stats
+                    ),
+                    DeviceProcessorStep(device=config.device),
+                ],
+            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
+        ),
+        PolicyProcessorPipeline[PolicyAction, PolicyAction](
+            steps=[DeviceProcessorStep(device="cpu")],
+            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
+            to_transition=policy_action_to_transition,
+            to_output=transition_to_policy_action,
+        ),
+    )

src/lerobot/policies/sarm/sarm_utils.py

@@ -0,0 +1,257 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from typing import Sequence, Any
+from pydantic import BaseModel, Field
+
+# Pydantic Models for SARM-style Annotation
+class Timestamp(BaseModel):
+    """Timestamp in MM:SS or SS format"""
+    start: str = Field(description="Start timestamp (MM:SS or just seconds)")
+    end: str = Field(description="End timestamp (MM:SS or just seconds)")
+
+
+class Subtask(BaseModel):
+    """Individual subtask/stage - must use EXACT names from provided list"""
+    name: str = Field(description="Subtask name - MUST match one from the predefined list exactly")
+    timestamps: Timestamp
+
+
+class SubtaskAnnotation(BaseModel):
+    """Complete annotation for a robot manipulation episode"""
+    subtasks: list[Subtask] = Field(description="List of all subtasks in temporal order")
+
+
+def compute_temporal_proportions(annotations: dict[int, Any], fps: int = 30) -> dict[str, float]:
+    """
+    Compute dataset-level temporal proportions (priors) for each subtask.
+    
+    Implements SARM Paper Formula (1):
+        ᾱ_k = (1/M) × Σ_i (L_{i,k} / T_i)
+    
+    where:
+        - M is the number of trajectories (episodes)
+        - L_{i,k} is the duration of subtask k in trajectory i
+        - T_i is the total duration of trajectory i
+    
+    This averages the PROPORTION of each subtask within each trajectory,
+    giving equal weight to all trajectories regardless of their absolute length.
+    
+    Args:
+        annotations: Dict mapping episode index to SubtaskAnnotation object.
+            Each annotation has a .subtasks list where each subtask has:
+            - .name: subtask name
+            - .timestamps.start: start time as "MM:SS" string
+            - .timestamps.end: end time as "MM:SS" string
+        fps: Frames per second (unused, kept for API compatibility)
+        
+    Returns:
+        Dict mapping subtask name to its temporal proportion (ᾱ_k).
+        Proportions are normalized to sum to 1.0.
+    """
+    subtask_proportions: dict[str, list[float]] = {}
+    
+    for annotation in annotations.values():
+        total_duration = 0
+        durations: dict[str, int] = {}
+        
+        for subtask in annotation.subtasks:
+            start_parts = subtask.timestamps.start.split(":")
+            end_parts = subtask.timestamps.end.split(":")
+            
+            start_seconds = int(start_parts[0]) * 60 + int(start_parts[1]) if len(start_parts) == 2 else int(start_parts[0])
+            end_seconds = int(end_parts[0]) * 60 + int(end_parts[1]) if len(end_parts) == 2 else int(end_parts[0])
+            
+            duration = end_seconds - start_seconds
+            durations[subtask.name] = duration
+            total_duration += duration
+        
+        # Calculate L_{i,k} / T_i for each subtask in this trajectory
+        if total_duration > 0:
+            for name, duration in durations.items():
+                if name not in subtask_proportions:
+                    subtask_proportions[name] = []
+                subtask_proportions[name].append(duration / total_duration)
+    
+    if not subtask_proportions:
+        return {}
+    
+    # Average across trajectories: (1/M) × Σ_i (L_{i,k} / T_i)
+    avg_proportions = {
+        name: sum(props) / len(props)
+        for name, props in subtask_proportions.items()
+    }
+    
+    # Normalize to ensure sum = 1
+    total = sum(avg_proportions.values())
+    if total > 0:
+        avg_proportions = {name: prop / total for name, prop in avg_proportions.items()}
+    
+    return avg_proportions
+
+
+def compute_tau(
+    current_frame: int | float,
+    subtask_start: int | float,
+    subtask_end: int | float,
+) -> float:
+    """
+    Compute within-subtask normalized time τ_t.
+    
+    Implements part of SARM Paper Formula (2):
+        τ_t = (t - s_k) / (e_k - s_k) ∈ [0, 1]
+    
+    where:
+        - t is the current frame
+        - s_k is the start frame of subtask k
+        - e_k is the end frame of subtask k
+    
+    Args:
+        current_frame: Current frame index (t)
+        subtask_start: Start frame of the subtask (s_k)
+        subtask_end: End frame of the subtask (e_k)
+        
+    Returns:
+        Within-subtask progress τ_t ∈ [0, 1]
+    """
+    subtask_duration = subtask_end - subtask_start
+    
+    if subtask_duration <= 0:
+        return 1.0
+    
+    tau = (current_frame - subtask_start) / subtask_duration
+    
+    return float(np.clip(tau, 0.0, 1.0))
+
+
+def compute_cumulative_progress_batch(
+    tau: torch.Tensor | float,
+    stage_indices: torch.Tensor | int,
+    alpha: torch.Tensor | Sequence[float],
+    cumulative_prior: torch.Tensor | None = None,
+) -> torch.Tensor | float:
+    """
+    Compute cumulative normalized progress from within-subtask progress.
+    
+    This function implements the core formula used in SARM for both:
+    
+    **Formula 2 (Training labels):**
+        y_t = P_{k-1} + ᾱ_k × τ_t ∈ [0, 1]
+        
+        Used to compute ground-truth progress labels from subtask annotations.
+        - τ_t comes from annotated frame position: τ_t = (t - s_k) / (e_k - s_k)
+        - k is the known subtask from annotations
+        
+    **Formula 4 (Inference predictions):**
+        ŷ_{1:N} = P̂_{k-1, 1:N} + ᾱ_{k, 1:N} × τ̂_{1:N} ∈ [0, 1]
+        
+        Used to convert model outputs to cumulative progress during inference.
+        - τ̂ comes from the subtask MLP head (conditioned on predicted stage)
+        - k = Ŝ is the predicted stage from Formula 3: Ŝ = argmax(softmax(Ψ))
+    
+    The formulas are mathematically identical; only the source of inputs differs:
+    - Training: τ and k from annotations → ground-truth labels
+    - Inference: τ̂ and Ŝ from model → predicted progress
+    
+    where:
+        - P_{k-1} = Σ_{j=1}^{k-1} ᾱ_j is the cumulative prior (sum of previous proportions)
+        - ᾱ_k is the temporal proportion for subtask k (from Formula 1)
+        - τ is within-subtask progress ∈ [0, 1]
+    
+    This ensures:
+        - y at start of subtask k = P_{k-1}
+        - y at end of subtask k = P_k
+    
+    Supports both scalar and batched tensor inputs:
+        - Scalar: tau (float), stage_indices (int), alpha (list/sequence)
+        - Batch: tau (Tensor), stage_indices (Tensor), alpha (Tensor), cumulative_prior (Tensor)
+    
+    Args:
+        tau: Within-subtask progress τ ∈ [0, 1]. 
+             For training: computed from frame position in annotated subtask.
+             For inference: predicted by subtask MLP head.
+             Scalar float or Tensor with shape (..., 1)
+        stage_indices: Index of current subtask k (0-indexed).
+             For training: known from annotations.
+             For inference: predicted via argmax(stage_probs) (Formula 3).
+             Scalar int or Tensor with shape (...)
+        alpha: Temporal proportions ᾱ with shape (num_stages,) or Sequence[float].
+             Computed from dataset annotations using Formula 1.
+        cumulative_prior: Optional. Cumulative priors P with shape (num_stages + 1,)
+             where cumulative_prior[k] = P_k = Σ_{j=1}^{k} ᾱ_j.
+             If None, will be computed from alpha.
+        
+    Returns:
+        Cumulative progress y ∈ [0, 1]. 
+        Scalar float if inputs are scalar, otherwise Tensor with shape (..., 1)
+    """    
+    if not isinstance(tau, torch.Tensor):
+        if not alpha:
+            raise ValueError("alpha (temporal_proportions) cannot be empty")
+        
+        if isinstance(alpha, torch.Tensor):
+            alpha_list = alpha.tolist()
+        else:
+            alpha_list = list(alpha)
+        
+        if stage_indices < 0 or stage_indices >= len(alpha_list):
+            raise ValueError(
+                f"stage_indices {stage_indices} out of range "
+                f"for {len(alpha_list)} subtasks"
+            )
+        
+        # P_{k-1} = sum of proportions for subtasks 0 to k-1
+        P_k_minus_1 = sum(alpha_list[:stage_indices])
+        
+        # ᾱ_k = proportion for current subtask
+        alpha_k = alpha_list[stage_indices]
+        
+        # y_t = P_{k-1} + ᾱ_k × τ_t
+        y_t = P_k_minus_1 + alpha_k * tau
+        
+        return float(np.clip(y_t, 0.0, 1.0))
+    
+    if not isinstance(alpha, torch.Tensor):
+        alpha = torch.tensor(alpha, dtype=torch.float32)
+    
+    # Compute cumulative_prior if not provided
+    if cumulative_prior is None:
+        cumulative_prior = torch.zeros(len(alpha) + 1, dtype=alpha.dtype, device=alpha.device)
+        cumulative_prior[1:] = torch.cumsum(alpha, dim=0)
+    
+    # P_{k-1} for each predicted stage
+    P_k_minus_1 = cumulative_prior[stage_indices]
+    
+    # ᾱ_k for each predicted stage
+    alpha_k = alpha[stage_indices]
+    
+    # ŷ = P_{k-1} + ᾱ_k × τ̂
+    progress = P_k_minus_1.unsqueeze(-1) + alpha_k.unsqueeze(-1) * tau
+    
+    return progress
+
+def pad_state_to_max_dim(state: torch.Tensor, max_state_dim: int) -> torch.Tensor:
+    """Pad the state tensor's last dimension to max_state_dim with zeros."""
+    current_dim = state.shape[-1]
+    if current_dim >= max_state_dim:
+        return state[..., :max_state_dim]  # Truncate if larger
+    
+    # Pad with zeros on the right
+    padding = (0, max_state_dim - current_dim)  # (left, right) for last dim
+    return F.pad(state, padding, mode='constant', value=0)

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -783,15 +783,18 @@ class VLAFlowMatching(nn.Module):
             use_cache=self.config.use_cache,
             fill_kv_cache=True,
         )
-        num_steps = self.config.num_steps
-        dt = -1.0 / num_steps
+        dt = -1.0 / self.config.num_steps
+        dt = torch.tensor(dt, dtype=torch.float32, device=device)
 
         x_t = noise
-        for step in range(num_steps):
-            time = 1.0 + step * dt
-            time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)
+        time = torch.tensor(1.0, dtype=torch.float32, device=device)
 
-            def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
+        while time >= -dt / 2:
+            expanded_time = time.expand(bsize)
+
+            # Define a closure function to properly capture expanded_time
+            # This avoids the lambda expression (E731) and loop variable binding (B023) issues
+            def denoise_step_partial_call(input_x_t, current_timestep=expanded_time):
                 return self.denoise_step(
                     x_t=input_x_t,
                     prefix_pad_masks=prefix_pad_masks,
@@ -815,11 +818,15 @@ class VLAFlowMatching(nn.Module):
             else:
                 v_t = denoise_step_partial_call(x_t)
 
-            x_t = x_t + dt * v_t
+            # Euler step
+            x_t += dt * v_t
 
+            # Record x_t and v_t after Euler step (other params are recorded in rtc_processor.denoise_step)
             if self.rtc_processor is not None and self.rtc_processor.is_debug_enabled():
                 self.rtc_processor.track(time=time, x_t=x_t, v_t=v_t)
 
+            time += dt
+
         return x_t
 
     def denoise_step(

src/lerobot/policies/utils.py

@@ -230,6 +230,10 @@ def validate_visual_features_consistency(
 ) -> None:
     """
     Validates visual feature consistency between a policy config and provided dataset/environment features.
+    
+    Validation passes if EITHER:
+    - Policy's expected visuals are a subset of dataset (policy uses some cameras, dataset has more)
+    - Dataset's provided visuals are a subset of policy (policy declares extras for flexibility)
 
     Args:
         cfg (PreTrainedConfig): The model or policy configuration containing input_features and type.
@@ -237,5 +241,11 @@ def validate_visual_features_consistency(
     """
     expected_visuals = {k for k, v in cfg.input_features.items() if v.type == FeatureType.VISUAL}
     provided_visuals = {k for k, v in features.items() if v.type == FeatureType.VISUAL}
-    if not provided_visuals.issubset(expected_visuals):
+    
+    # Accept if either direction is a subset 
+    policy_subset_of_dataset = expected_visuals.issubset(provided_visuals)
+    dataset_subset_of_policy = provided_visuals.issubset(expected_visuals)
+    
+    if not (policy_subset_of_dataset or dataset_subset_of_policy):
         raise_feature_mismatch_error(provided_visuals, expected_visuals)
+        

src/lerobot/policies/xvla/__init__.py

@@ -1,6 +0,0 @@
-# register the processor steps
-from lerobot.policies.xvla.processor_xvla import (
-    XVLAAddDomainIdProcessorStep,
-    XVLAImageNetNormalizeProcessorStep,
-    XVLAImageToFloatProcessorStep,
-)

src/lerobot/policies/xvla/action_hub.py

@@ -1,588 +0,0 @@
-# ------------------------------------------------------------------------------
-# Copyright 2025 2toINF and HuggingFace Inc. (https://github.com/2toINF)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ------------------------------------------------------------------------------
-
-from __future__ import annotations
-
-from collections.abc import Iterable
-
-import torch
-import torch.nn as nn
-
-# =============================================================================
-# Registry
-# =============================================================================
-ACTION_REGISTRY: dict[str, type[BaseActionSpace]] = {}
-
-
-def register_action(name: str):
-    """Decorator for registering a new action space."""
-
-    def _wrap(cls):
-        key = name.lower()
-        if key in ACTION_REGISTRY:
-            raise KeyError(f"ActionSpace '{key}' already registered -> {ACTION_REGISTRY[key]}")
-        ACTION_REGISTRY[key] = cls
-        cls.name = key
-        return cls
-
-    return _wrap
-
-
-def build_action_space(name: str, **kwargs) -> BaseActionSpace:
-    """Instantiate a registered action space by name."""
-    key = name.lower()
-    if key not in ACTION_REGISTRY:
-        raise KeyError(f"Unknown action space '{name}'. Available: {list(ACTION_REGISTRY.keys())}")
-    return ACTION_REGISTRY[key](**kwargs)
-
-
-# =============================================================================
-# Base class
-# =============================================================================
-class BaseActionSpace(nn.Module):
-    """
-    Abstract base class for all action-space definitions.
-
-    Each subclass defines:
-      - `dim_action`: dimension of the action vector.
-      - `gripper_idx`: indices of gripper channels.
-      - `compute_loss(pred, target)`: supervised loss for this space.
-      - `preprocess(proprio, action, mode)`: pre-step modifications.
-      - `postprocess(action)`: post-step corrections (e.g. apply sigmoid).
-    """
-
-    name: str = "base"
-    dim_action: int = 0
-    gripper_idx: tuple[int, ...] = ()
-
-    def __init__(self):
-        super().__init__()
-
-    # ---------------------------------------------------------------------
-    # Core supervised loss
-    # ---------------------------------------------------------------------
-    def compute_loss(self, pred: torch.Tensor, target: torch.Tensor) -> dict[str, torch.Tensor]:
-        raise NotImplementedError
-
-    def forward(self, pred: torch.Tensor, target: torch.Tensor) -> dict[str, torch.Tensor]:
-        """Alias for compute_loss."""
-        return self.compute_loss(pred, target)
-
-    # ---------------------------------------------------------------------
-    # Space-level hooks
-    # ---------------------------------------------------------------------
-    def preprocess(
-        self,
-        proprio: torch.Tensor,
-        action: torch.Tensor,
-        mode: str = "train",
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """Default: return unchanged."""
-        return proprio, action
-
-    def postprocess(self, action: torch.Tensor) -> torch.Tensor:
-        """Default: return unchanged."""
-        return action
-
-
-# =============================================================================
-# Utilities
-# =============================================================================
-def _ensure_indices_valid(dim_action: int, idx: Iterable[int], name: str) -> None:
-    bad = [i for i in idx if i < 0 or i >= dim_action]
-    if bad:
-        raise IndexError(f"{name} contains out-of-range indices {bad} for action dim dim_action={dim_action}")
-
-
-# =============================================================================
-# Implementations
-# =============================================================================
-@register_action("ee6d")
-class EE6DActionSpace(BaseActionSpace):
-    """End-effector layout with xyz, 6D rotation, and gripper channels."""
-
-    dim_action = 20
-    gripper_idx = (9, 19)
-    GRIPPER_SCALE = 1.0
-    XYZ_SCALE = 500.0
-    ROT_SCALE = 10.0
-
-    POS_IDX_1 = (0, 1, 2)
-    POS_IDX_2 = (10, 11, 12)
-    ROT_IDX_1 = (3, 4, 5, 6, 7, 8)
-    ROT_IDX_2 = (13, 14, 15, 16, 17, 18)
-
-    def __init__(self):
-        super().__init__()
-        self.mse = nn.MSELoss()
-        self.bce = nn.BCEWithLogitsLoss()
-
-    def compute_loss(self, pred, target):
-        assert pred.shape == target.shape, "pred/target shapes must match"
-        batch_size, seq_len, action_dim = pred.shape
-        _ensure_indices_valid(action_dim, self.gripper_idx, "gripper_idx")
-
-        # Gripper BCE
-        g_losses = [self.bce(pred[:, :, gi], target[:, :, gi]) for gi in self.gripper_idx]
-        gripper_loss = sum(g_losses) / len(self.gripper_idx) * self.GRIPPER_SCALE
-
-        # XYZ position
-        pos_loss = (
-            self.mse(pred[:, :, self.POS_IDX_1], target[:, :, self.POS_IDX_1])
-            + self.mse(pred[:, :, self.POS_IDX_2], target[:, :, self.POS_IDX_2])
-        ) * self.XYZ_SCALE
-
-        # Rotation 6D
-        rot_loss = (
-            self.mse(pred[:, :, self.ROT_IDX_1], target[:, :, self.ROT_IDX_1])
-            + self.mse(pred[:, :, self.ROT_IDX_2], target[:, :, self.ROT_IDX_2])
-        ) * self.ROT_SCALE
-
-        return {
-            "position_loss": pos_loss,
-            "rotate6D_loss": rot_loss,
-            "gripper_loss": gripper_loss,
-        }
-
-    def preprocess(self, proprio, action, mode="train"):
-        """Zero-out gripper channels in proprio/action."""
-        proprio_m = proprio.clone()
-        action_m = action.clone()
-        proprio_m[..., self.gripper_idx] = 0.0
-        action_m[..., self.gripper_idx] = 0.0
-        return proprio_m, action_m
-
-    def postprocess(self, action: torch.Tensor) -> torch.Tensor:
-        """Apply sigmoid to gripper logits."""
-        if action.size(-1) > max(self.gripper_idx):
-            action[..., self.gripper_idx] = torch.sigmoid(action[..., self.gripper_idx])
-        return action
-
-
-@register_action("joint")
-class JointActionSpace(BaseActionSpace):
-    """Joint-space layout with joints + gripper only."""
-
-    dim_action = 14
-    gripper_idx = (6, 13)
-    GRIPPER_SCALE = 0.1
-    JOINTS_SCALE = 1.0
-
-    def __init__(self):
-        super().__init__()
-        self.mse = nn.MSELoss()
-        self.bce = nn.BCEWithLogitsLoss()
-
-    def compute_loss(self, pred, target):
-        assert pred.shape == target.shape
-        batch_size, seq_len, action_dim = pred.shape
-        _ensure_indices_valid(action_dim, self.gripper_idx, "gripper_idx")
-
-        g_losses = [self.bce(pred[:, :, gi], target[:, :, gi]) for gi in self.gripper_idx]
-        gripper_loss = sum(g_losses) / len(self.gripper_idx) * self.GRIPPER_SCALE
-
-        joints_idx = tuple(i for i in range(action_dim) if i not in set(self.gripper_idx))
-        joints_loss = self.mse(pred[:, :, joints_idx], target[:, :, joints_idx]) * self.JOINTS_SCALE
-
-        return {
-            "joints_loss": joints_loss,
-            "gripper_loss": gripper_loss,
-        }
-
-    def preprocess(self, proprio, action, mode="train"):
-        """Zero-out gripper channels in proprio/action."""
-        proprio_m = proprio.clone()
-        action_m = action.clone()
-        proprio_m[..., self.gripper_idx] = 0.0
-        action_m[..., self.gripper_idx] = 0.0
-        return proprio_m, action_m
-
-    def postprocess(self, action: torch.Tensor) -> torch.Tensor:
-        """Apply sigmoid to gripper logits."""
-        if action.size(-1) > max(self.gripper_idx):
-            action[..., self.gripper_idx] = torch.sigmoid(action[..., self.gripper_idx])
-        return action
-
-
-@register_action("agibot_ee6d")
-class AGIBOTEE6DActionSpace(BaseActionSpace):
-    """AGI-bot variant of EE6DActionSpace using MSE for all components."""
-
-    dim_action = 20
-    gripper_idx = (9, 19)
-    GRIPPER_SCALE = 10.0
-    XYZ_SCALE = 500.0
-    ROT_SCALE = 10.0
-    POS_IDX_1 = (0, 1, 2)
-    POS_IDX_2 = (10, 11, 12)
-    ROT_IDX_1 = (3, 4, 5, 6, 7, 8)
-    ROT_IDX_2 = (13, 14, 15, 16, 17, 18)
-
-    def __init__(self):
-        super().__init__()
-        self.mse = nn.MSELoss()
-
-    def compute_loss(self, pred, target):
-        assert pred.shape == target.shape
-        batch_size, seq_len, action_dim = pred.shape
-        _ensure_indices_valid(action_dim, self.gripper_idx, "gripper_idx")
-
-        gripper_loss = (
-            self.mse(pred[:, :, self.gripper_idx], target[:, :, self.gripper_idx]) * self.GRIPPER_SCALE
-        )
-        pos_loss = (
-            self.mse(pred[:, :, self.POS_IDX_1], target[:, :, self.POS_IDX_1])
-            + self.mse(pred[:, :, self.POS_IDX_2], target[:, :, self.POS_IDX_2])
-        ) * self.XYZ_SCALE
-        rot_loss = (
-            self.mse(pred[:, :, self.ROT_IDX_1], target[:, :, self.ROT_IDX_1])
-            + self.mse(pred[:, :, self.ROT_IDX_2], target[:, :, self.ROT_IDX_2])
-        ) * self.ROT_SCALE
-
-        return {
-            "position_loss": pos_loss,
-            "rotate6D_loss": rot_loss,
-            "gripper_loss": gripper_loss,
-        }
-
-    def preprocess(self, proprio, action, mode="train"):
-        """No preprocessing applied in AGIBOT variant."""
-        return proprio, action
-
-    def postprocess(self, action: torch.Tensor) -> torch.Tensor:
-        """AGIBOT does not postprocess."""
-        return action
-
-
-@register_action("franka_joint7")
-class FrankaJoint7ActionSpace(BaseActionSpace):
-    """
-    Franka Panda joint-space: 7 joints, with gripper.
-
-    - Real robot action dim: 7
-    - Model-facing dim: 20 (padded with zeros)
-      compatible with pretrained VLA models expecting 20D.
-    """
-
-    dim_action = 20  # model dimension
-    REAL_DIM = 7  # actual Franka joints
-
-    JOINTS_SCALE = 1.0
-
-    def __init__(self):
-        super().__init__()
-        self.mse = nn.MSELoss()
-
-    def _pad_to_model_dim(self, x: torch.Tensor) -> torch.Tensor:
-        """Pad 7 → 20 dims (zeros for the dummy channels)."""
-        if x is None:
-            return None
-        if x.size(-1) == self.dim_action:
-            return x
-        if x.size(-1) != self.REAL_DIM:
-            raise ValueError(
-                f"Expected last dim to be {self.REAL_DIM} or {self.dim_action}, got {x.size(-1)}"
-            )
-
-        pad_shape = list(x.shape[:-1]) + [self.dim_action - self.REAL_DIM]  # 13 zeros
-        pad = x.new_zeros(pad_shape)
-        return torch.cat([x, pad], dim=-1)
-
-    def _trim_to_real_dim(self, x: torch.Tensor) -> torch.Tensor:
-        """Trim model output 20 → 7 dims."""
-        return x[..., : self.REAL_DIM]
-
-    def compute_loss(self, pred, target):
-        """
-        pred :  [B, T, 20]
-        target : [B, T, 7] or [B, T, 20]
-
-        Only compute MSE on the first 7 dims.
-        """
-        pred = self._pad_to_model_dim(pred)
-        target = self._pad_to_model_dim(target)
-
-        assert pred.shape == target.shape
-
-        joints_loss = (
-            self.mse(
-                pred[:, :, : self.REAL_DIM],  # use only the first 7 joints
-                target[:, :, : self.REAL_DIM],
-            )
-            * self.JOINTS_SCALE
-        )
-
-        return {"joints_loss": joints_loss}
-
-    def preprocess(self, proprio, action, mode="train"):
-        """
-        During training:
-        - Pad [7] → [20]
-        """
-        return proprio, self._pad_to_model_dim(action)
-
-    def postprocess(self, action: torch.Tensor) -> torch.Tensor:
-        """
-        After model prediction:
-        - Trim [20] → [7] for real robot control.
-        """
-        return self._trim_to_real_dim(action)
-
-
-@register_action("auto")
-class AutoActionSpace(BaseActionSpace):
-    """
-    Auto-detecting action space that adapts to any action dimension.
-
-    - Auto-detects the real action dimension from the policy feature
-    - Model outputs max_dim for compatibility with pretrained models
-    - Loss is computed only on the first real_dim dimensions
-    - Postprocess trims output back to real_dim
-
-    Args:
-        real_dim: The actual action dimension from the dataset/policy feature
-        max_dim: The model's output dimension for pretrained VLA compatibility
-    """
-
-    JOINTS_SCALE = 1.0
-
-    def __init__(self, real_dim: int, max_dim: int):
-        super().__init__()
-        self.real_dim = real_dim
-        self.dim_action = max_dim  # Model-facing dimension
-        self.mse = nn.MSELoss()
-
-    def _pad_to_model_dim(self, x: torch.Tensor) -> torch.Tensor:
-        """Pad real_dim → max_dim (zeros for the dummy channels)."""
-        if x is None:
-            return None
-        if x.size(-1) == self.dim_action:
-            return x
-        if x.size(-1) != self.real_dim:
-            # If dimension doesn't match either, pad/trim to real_dim first
-            if x.size(-1) < self.real_dim:
-                pad_shape = list(x.shape[:-1]) + [self.real_dim - x.size(-1)]
-                pad = x.new_zeros(pad_shape)
-                x = torch.cat([x, pad], dim=-1)
-            else:
-                x = x[..., : self.real_dim]
-
-        pad_shape = list(x.shape[:-1]) + [self.dim_action - self.real_dim]
-        pad = x.new_zeros(pad_shape)
-        return torch.cat([x, pad], dim=-1)
-
-    def _trim_to_real_dim(self, x: torch.Tensor) -> torch.Tensor:
-        """Trim model output max_dim → real_dim."""
-        return x[..., : self.real_dim]
-
-    def compute_loss(self, pred: torch.Tensor, target: torch.Tensor) -> dict[str, torch.Tensor]:
-        """
-        Compute loss only on the first real_dim dimensions.
-
-        pred:   [B, T, max_dim] from the model
-        target: [B, T, real_dim] or [B, T, max_dim]
-
-        Loss = MSE(pred[:,:,:real_dim], target[:,:,:real_dim])
-        """
-        pred = self._pad_to_model_dim(pred)
-        target = self._pad_to_model_dim(target)
-        assert pred.shape == target.shape, f"Shape mismatch: pred {pred.shape} vs target {target.shape}"
-
-        # only compute loss on the real dimensions
-        joints_loss = (
-            self.mse(
-                pred[:, :, : self.real_dim],
-                target[:, :, : self.real_dim],
-            )
-            * self.JOINTS_SCALE
-        )
-
-        return {"joints_loss": joints_loss}
-
-    def preprocess(self, proprio: torch.Tensor, action: torch.Tensor, mode: str = "train"):
-        """
-        Pad action from real_dim to max_dim for the model.
-        """
-        return proprio, self._pad_to_model_dim(action)
-
-    def postprocess(self, action: torch.Tensor) -> torch.Tensor:
-        """
-        Trim model output from max_dim to real_dim for real robot control.
-        """
-        return self._trim_to_real_dim(action)
-
-
-@register_action("so101_bimanual")
-class BimanualSO101ActionSpace(BaseActionSpace):
-    """
-    Bimanual SO101 robot: 2 arms with 5 joints each + gripper.
-
-    Layout (real robot):
-    [left_arm (5 joints + gripper), right_arm (5 joints + gripper)]
-    - Left arm:  shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll, gripper
-    - Right arm: shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll, gripper
-
-    Real action dim: 12
-    Model-facing dim: 20 (extra 8 dummy dims at the end)
-    """
-
-    # Model output / training dimension (to match pretrained policy)
-    dim_action = 20
-
-    # Real robot action dimension
-    REAL_DIM = 12
-
-    # Indices of real vs dummy channels
-    REAL_IDXS = tuple(range(REAL_DIM))  # 0..11
-    DUMMY_IDXS = tuple(range(REAL_DIM, dim_action))  # 12..19
-
-    # Grippers live in the real part
-    gripper_idx = (5, 11)  # left_gripper at idx 5, right_gripper at idx 11
-    GRIPPER_SCALE = 1.0
-    JOINTS_SCALE = 1.0
-
-    # Indices for left and right arm joints (excluding grippers)
-    LEFT_ARM_JOINTS = (0, 1, 2, 3, 4)
-    RIGHT_ARM_JOINTS = (6, 7, 8, 9, 10)
-
-    def __init__(self):
-        super().__init__()
-        self.mse = nn.MSELoss()
-        self.bce = nn.BCEWithLogitsLoss()
-
-    # ---------- helpers ----------
-
-    def _pad_to_model_dim(self, x: torch.Tensor) -> torch.Tensor:
-        """If last dim is REAL_DIM (12), pad zeros to reach dim_action (20)."""
-        if x is None:
-            return None
-        if x.size(-1) == self.dim_action:
-            return x
-        if x.size(-1) != self.REAL_DIM:
-            raise ValueError(
-                f"Expected last dim to be {self.REAL_DIM} or {self.dim_action}, got {x.size(-1)}"
-            )
-        pad_shape = list(x.shape[:-1]) + [self.dim_action - self.REAL_DIM]
-        pad = x.new_zeros(pad_shape)
-        return torch.cat([x, pad], dim=-1)
-
-    def _trim_to_real_dim(self, x: torch.Tensor) -> torch.Tensor:
-        """Keep only the first REAL_DIM (12) dims for the real robot."""
-        return x[..., : self.REAL_DIM]
-
-    # ---------- loss ----------
-
-    def compute_loss(self, pred, target):
-        """
-        pred:  [B, T, 20] from the model
-        target: [B, T, 12] or [B, T, 20]
-        We pad target → 20 and compute loss only on the real dims.
-        """
-        # Ensure both are [B, T, 20]
-        pred = self._pad_to_model_dim(pred)
-        target = self._pad_to_model_dim(target)
-        assert pred.shape == target.shape
-
-        # ---- MSE for all real dims (0–11) ----
-        real_dims = 12
-
-        joints_loss = (
-            self.mse(
-                pred[:, :, :real_dims],
-                target[:, :, :real_dims],
-            )
-            * self.JOINTS_SCALE
-        )
-
-        left_arm_loss = self.mse(pred[:, :, :6], target[:, :, :6])
-        right_arm_loss = self.mse(pred[:, :, 6:12], target[:, :, 6:12])
-
-        gripper_loss = (
-            self.mse(
-                pred[:, :, [5, 11]],
-                target[:, :, [5, 11]],
-            )
-            * self.GRIPPER_SCALE
-        )
-
-        return {
-            "joints_loss": joints_loss,
-            "gripper_loss": gripper_loss,
-            "left_arm_loss": left_arm_loss,
-            "right_arm_loss": right_arm_loss,
-        }
-
-    # ---------- preprocess / postprocess ----------
-
-    def preprocess(self, proprio, action, mode="train"):
-        """
-        - If proprio/action are 12-dim, pad them to 20 for the model.
-        - Zero-out gripper channels in proprio/action to focus learning on joints.
-        """
-        proprio_m = self._pad_to_model_dim(proprio.clone())
-        action_m = self._pad_to_model_dim(action.clone()) if action is not None else None
-
-        proprio_m[..., self.gripper_idx] = 0.0
-        if action_m is not None:
-            action_m[..., self.gripper_idx] = 0.0
-
-        return proprio_m, action_m
-
-    def postprocess(self, action: torch.Tensor) -> torch.Tensor:
-        """
-        - Model outputs [*, 20]
-        - Apply sigmoid to gripper logits
-        - Return only the first 12 dims for the real robot:
-          ["left_shoulder_pan.pos",
-           "left_shoulder_lift.pos",
-           "left_elbow_flex.pos",
-           "left_wrist_flex.pos",
-           "left_wrist_roll.pos",
-           "left_gripper.pos",
-           "right_shoulder_pan.pos",
-           "right_shoulder_lift.pos",
-           "right_elbow_flex.pos",
-           "right_wrist_flex.pos",
-           "right_wrist_roll.pos",
-           "right_gripper.pos"]
-        """
-        # Ensure we at least have the real dims + grippers
-        if action.size(-1) < self.REAL_DIM:
-            raise ValueError(f"Expected at least {self.REAL_DIM} dims in action, got {action.size(-1)}")
-
-        # Apply sigmoid on gripper channels in model space (indices 5 and 11)
-        if action.size(-1) > max(self.gripper_idx):
-            action[..., self.gripper_idx] = torch.sigmoid(action[..., self.gripper_idx])
-
-        # Return only the real 12-dim control vector for the env
-        return self._trim_to_real_dim(action)
-
-
-# =============================================================================
-# Exports
-# =============================================================================
-__all__ = [
-    "BaseActionSpace",
-    "build_action_space",
-    "register_action",
-    "EE6DActionSpace",
-    "JointActionSpace",
-    "AGIBOTEE6DActionSpace",
-    "FrankaJoint7ActionSpace",
-    "AutoActionSpace",
-    "BimanualSO101ActionSpace",
-    "ACTION_REGISTRY",
-]

src/lerobot/policies/xvla/configuration_florence2.py

@@ -1,353 +0,0 @@
-# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import warnings
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-""" Florence-2 configuration"""
-
-logger = logging.get_logger(__name__)
-
-
-class Florence2VisionConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`Florence2VisionModel`]. It is used to instantiate a Florence2VisionModel
-    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
-    defaults will yield a similar configuration to that of the Florence2VisionModel architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        drop_path_rate (`float`, *optional*, defaults to 0.1):
-            The dropout rate of the drop path layer.
-        patch_size (`List[int]`, *optional*, defaults to [7, 3, 3, 3]):
-            The patch size of the image.
-        patch_stride (`List[int]`, *optional*, defaults to [4, 2, 2, 2]):
-            The patch stride of the image.
-        patch_padding (`List[int]`, *optional*, defaults to [3, 1, 1, 1]):
-            The patch padding of the image.
-        patch_prenorm (`List[bool]`, *optional*, defaults to [false, true, true, true]):
-            Whether to apply layer normalization before the patch embedding layer.
-        enable_checkpoint (`bool`, *optional*, defaults to False):
-            Whether to enable checkpointing.
-        dim_embed (`List[int]`, *optional*, defaults to [256, 512, 1024, 2048]):
-            The dimension of the embedding layer.
-        num_heads (`List[int]`, *optional*, defaults to [8, 16, 32, 64]):
-            The number of attention heads.
-        num_groups (`List[int]`, *optional*, defaults to [8, 16, 32, 64]):
-            The number of groups.
-        depths (`List[int]`, *optional*, defaults to [1, 1, 9, 1]):
-            The depth of the model.
-        window_size (`int`, *optional*, defaults to 12):
-            The window size of the model.
-        projection_dim (`int`, *optional*, defaults to 1024):
-            The dimension of the projection layer.
-        visual_temporal_embedding (`dict`, *optional*):
-            The configuration of the visual temporal embedding.
-        image_pos_embed (`dict`, *optional*):
-            The configuration of the image position embedding.
-        image_feature_source (`List[str]`, *optional*, defaults to ["spatial_avg_pool", "temporal_avg_pool"]):
-            The source of the image feature.
-    Example:
-
-    ```python
-    >>> from transformers import Florence2VisionConfig, Florence2VisionModel
-
-    >>> # Initializing a Florence2 Vision style configuration
-    >>> configuration = Florence2VisionConfig()
-
-    >>> # Initializing a model (with random weights)
-    >>> model = Florence2VisionModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "davit"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        drop_path_rate=0.1,
-        patch_size=None,
-        patch_stride=None,
-        patch_padding=None,
-        patch_prenorm=None,
-        enable_checkpoint=False,
-        dim_embed=None,
-        num_heads=None,
-        num_groups=None,
-        depths=None,
-        window_size=12,
-        projection_dim=1024,
-        visual_temporal_embedding=None,
-        image_pos_embed=None,
-        image_feature_source=None,
-        **kwargs,
-    ):
-        self.drop_path_rate = drop_path_rate
-        self.patch_size = patch_size if patch_size is not None else [7, 3, 3, 3]
-        self.patch_stride = patch_stride if patch_stride is not None else [4, 2, 2, 2]
-        self.patch_padding = patch_padding if patch_padding is not None else [3, 1, 1, 1]
-        self.patch_prenorm = patch_prenorm if patch_prenorm is not None else [False, True, True, True]
-        self.enable_checkpoint = enable_checkpoint
-        self.dim_embed = dim_embed if dim_embed is not None else [256, 512, 1024, 2048]
-        self.num_heads = num_heads if num_heads is not None else [8, 16, 32, 64]
-        self.num_groups = num_groups if num_groups is not None else [8, 16, 32, 64]
-        self.depths = depths if depths is not None else [1, 1, 9, 1]
-        self.window_size = window_size
-        self.projection_dim = projection_dim
-
-        if visual_temporal_embedding is None:
-            visual_temporal_embedding = {
-                "type": "COSINE",
-                "max_temporal_embeddings": 100,
-            }
-        self.visual_temporal_embedding = visual_temporal_embedding
-
-        if image_pos_embed is None:
-            image_pos_embed = {
-                "type": "learned_abs_2d",
-                "max_pos_embeddings": 1000,
-            }
-        self.image_pos_embed = image_pos_embed
-
-        self.image_feature_source = (
-            image_feature_source
-            if image_feature_source is not None
-            else ["spatial_avg_pool", "temporal_avg_pool"]
-        )
-
-        super().__init__(**kwargs)
-
-
-class Florence2LanguageConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`Florence2LanguagePreTrainedModel`]. It is used to instantiate a BART
-    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
-    defaults will yield a similar configuration to that of the BART
-    [facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 51289):
-            Vocabulary size of the Florence2Language model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`Florence2LanguageModel`].
-        d_model (`int`, *optional*, defaults to 1024):
-            Dimensionality of the layers and the pooler layer.
-        encoder_layers (`int`, *optional*, defaults to 12):
-            Number of encoder layers.
-        decoder_layers (`int`, *optional*, defaults to 12):
-            Number of decoder layers.
-        encoder_attention_heads (`int`, *optional*, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        decoder_attention_heads (`int`, *optional*, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
-        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
-        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"silu"` and `"gelu_new"` are supported.
-        dropout (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        activation_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for activations inside the fully connected layer.
-        classifier_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for classifier.
-        max_position_embeddings (`int`, *optional*, defaults to 1024):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        init_std (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
-            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
-            for more details.
-        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
-            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
-            for more details.
-        scale_embedding (`bool`, *optional*, defaults to `False`):
-            Scale embeddings by diving by sqrt(d_model).
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models).
-        num_labels (`int`, *optional*, defaults to 3):
-            The number of labels to use in [`Florence2LanguageForSequenceClassification`].
-        forced_eos_token_id (`int`, *optional*, defaults to 2):
-            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
-            `eos_token_id`.
-
-    Example:
-
-    ```python
-    >>> from transformers import Florence2LanguageConfig, Florence2LanguageModel
-
-    >>> # Initializing a Florence2 Language style configuration
-    >>> configuration = Florence2LanguageConfig()
-
-    >>> # Initializing a model (with random weights)
-    >>> model = Florence2LanguageModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "florence2_language"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
-
-    def __init__(
-        self,
-        vocab_size=51289,
-        max_position_embeddings=1024,
-        encoder_layers=12,
-        encoder_ffn_dim=4096,
-        encoder_attention_heads=16,
-        decoder_layers=12,
-        decoder_ffn_dim=4096,
-        decoder_attention_heads=16,
-        encoder_layerdrop=0.0,
-        decoder_layerdrop=0.0,
-        activation_function="gelu",
-        d_model=1024,
-        dropout=0.1,
-        attention_dropout=0.0,
-        activation_dropout=0.0,
-        init_std=0.02,
-        classifier_dropout=0.0,
-        scale_embedding=False,
-        use_cache=True,
-        num_labels=3,
-        pad_token_id=1,
-        bos_token_id=0,
-        eos_token_id=2,
-        is_encoder_decoder=True,
-        decoder_start_token_id=2,
-        forced_eos_token_id=2,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.d_model = d_model
-        self.encoder_ffn_dim = encoder_ffn_dim
-        self.encoder_layers = encoder_layers
-        self.encoder_attention_heads = encoder_attention_heads
-        self.decoder_ffn_dim = decoder_ffn_dim
-        self.decoder_layers = decoder_layers
-        self.decoder_attention_heads = decoder_attention_heads
-        self.dropout = dropout
-        self.attention_dropout = attention_dropout
-        self.activation_dropout = activation_dropout
-        self.activation_function = activation_function
-        self.init_std = init_std
-        self.encoder_layerdrop = encoder_layerdrop
-        self.decoder_layerdrop = decoder_layerdrop
-        self.classifier_dropout = classifier_dropout
-        self.use_cache = use_cache
-        self.num_hidden_layers = encoder_layers
-        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
-
-        super().__init__(
-            num_labels=num_labels,
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            is_encoder_decoder=is_encoder_decoder,
-            decoder_start_token_id=decoder_start_token_id,
-            forced_eos_token_id=forced_eos_token_id,
-            **kwargs,
-        )
-
-        # ensure backward compatibility for BART CNN models
-        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
-            self.forced_bos_token_id = self.bos_token_id
-            warnings.warn(
-                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
-                "The config can simply be saved and uploaded again to be fixed.",
-                stacklevel=2,
-            )
-
-
-class Florence2Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`Florence2ForConditionalGeneration`]. It is used to instantiate an
-    Florence-2 model according to the specified arguments, defining the model architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        vision_config (`Florence2VisionConfig`,  *optional*):
-            Custom vision config or dict
-        text_config (`Union[AutoConfig, dict]`, *optional*):
-            The config object of the text backbone.
-        ignore_index (`int`, *optional*, defaults to -100):
-            The ignore index for the loss function.
-        vocab_size (`int`, *optional*, defaults to 51289):
-            Vocabulary size of the Florence2model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`~Florence2ForConditionalGeneration`]
-        projection_dim (`int`, *optional*, defaults to 1024):
-            Dimension of the multimodal projection space.
-
-    Example:
-
-    ```python
-    >>> from transformers import Florence2ForConditionalGeneration, Florence2Config, CLIPVisionConfig, BartConfig
-
-    >>> # Initializing a clip-like vision config
-    >>> vision_config = CLIPVisionConfig()
-
-    >>> # Initializing a Bart config
-    >>> text_config = BartConfig()
-
-    >>> # Initializing a Florence-2 configuration
-    >>> configuration = Florence2Config(vision_config, text_config)
-
-    >>> # Initializing a model from the florence-2 configuration
-    >>> model = Florence2ForConditionalGeneration(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "florence2"
-    is_composition = False
-
-    def __init__(
-        self,
-        vision_config=None,
-        text_config=None,
-        ignore_index=-100,
-        vocab_size=51289,
-        projection_dim=1024,
-        **kwargs,
-    ):
-        self.ignore_index = ignore_index
-        self.vocab_size = vocab_size
-        self.projection_dim = projection_dim
-        if vision_config is not None:
-            vision_config = Florence2VisionConfig(**vision_config)
-        self.vision_config = vision_config
-
-        self.text_config = text_config
-        if text_config is not None:
-            self.text_config = Florence2LanguageConfig(**text_config)
-
-        super().__init__(**kwargs)

src/lerobot/policies/xvla/configuration_xvla.py

@@ -1,203 +0,0 @@
-#!/usr/bin/env python
-
-# ------------------------------------------------------------------------------
-# Copyright 2025 The HuggingFace Inc. team and 2toINF (https://github.com/2toINF)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ------------------------------------------------------------------------------
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any
-
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-from lerobot.optim.optimizers import XVLAAdamWConfig
-from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
-from lerobot.utils.constants import OBS_IMAGES
-
-# Conditional import for type checking and lazy loading
-from lerobot.utils.import_utils import _transformers_available
-
-if TYPE_CHECKING or _transformers_available:
-    from .configuration_florence2 import Florence2Config
-else:
-    Florence2Config = None
-
-
-@PreTrainedConfig.register_subclass("xvla")
-@dataclass
-class XVLAConfig(PreTrainedConfig):
-    """
-    Configuration class for the XVLA (Extended Vision-Language-Action) policy so it can
-    plug into the LeRobot training stack.
-
-    The config mirrors the knobs exposed in the original XVLA repository but also
-    declares the input/output feature contract required by LeRobot.
-    """
-
-    # Input / output structure
-    n_obs_steps: int = 1
-    chunk_size: int = 32
-    n_action_steps: int = 32
-    dtype: str = "float32"  # Options: "bfloat16", "float32"
-
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.IDENTITY,
-            "ACTION": NormalizationMode.IDENTITY,
-        }
-    )
-
-    # Florence2 backbone and tokenizer configuration
-    florence_config: dict[str, Any] = field(default_factory=dict)
-    tokenizer_name: str = "facebook/bart-large"
-    tokenizer_max_length: int = 64
-    tokenizer_padding_side: str = "right"
-    pad_language_to: str = "max_length"
-
-    # Transformer head
-    hidden_size: int = 1024
-    depth: int = 24
-    num_heads: int = 16
-    mlp_ratio: float = 4.0
-    num_domains: int = 30
-    len_soft_prompts: int = 32
-    dim_time: int = 32
-    max_len_seq: int = 512
-    use_hetero_proj: bool = False
-
-    # Action & proprioception
-    action_mode: str = "ee6d"
-    num_denoising_steps: int = 10
-    use_proprio: bool = True
-    max_state_dim: int = 32
-    max_action_dim: int = 20  # Maximum action dimension for padding (used by "auto" action mode)
-    domain_feature_key: str | None = None
-
-    # Vision preprocessing
-    resize_imgs_with_padding: tuple[int, int] | None = None
-    num_image_views: int | None = None
-    empty_cameras: int = 0
-
-    # Freezing options for VLM components
-    # By default, VLM encoders are frozen and only policy transformer + soft prompts train
-    freeze_vision_encoder: bool = False  # Freeze VLM vision encoder weights
-    freeze_language_encoder: bool = False  # Freeze VLM language encoder weights
-    train_policy_transformer: bool = True  # Allow policy transformer to train
-    train_soft_prompts: bool = True  # Allow soft prompts to train
-
-    # Training presets
-    optimizer_lr: float = 1e-4
-    optimizer_betas: tuple[float, float] = (0.9, 0.99)
-    optimizer_eps: float = 1e-8
-    optimizer_weight_decay: float = 0.0
-    optimizer_grad_clip_norm: float = 10.0
-    # Soft-prompt LR settings (for optional warm-up)
-    optimizer_soft_prompt_lr_scale: float = 1.0  # Scale factor for soft-prompt LR
-    optimizer_soft_prompt_warmup_lr_scale: float | None = None  # Start scale for warmup (e.g., 0.01)
-
-    scheduler_warmup_steps: int = 1_000
-    scheduler_decay_steps: int = 30_000
-    scheduler_decay_lr: float = 2.5e-6
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-
-        if self.chunk_size <= 0:
-            raise ValueError("`chunk_size` must be strictly positive.")
-        if self.n_action_steps > self.chunk_size:
-            raise ValueError(
-                f"`n_action_steps` ({self.n_action_steps}) must be <= `chunk_size` ({self.chunk_size})."
-            )
-        if self.num_image_views is not None and self.num_image_views <= 0:
-            raise ValueError("`num_image_views` must be > 0 when specified.")
-        if self.dtype not in ["bfloat16", "float32"]:
-            raise ValueError(f"Invalid dtype: {self.dtype}")
-        self._florence_config_obj: Florence2Config | None = None
-
-    def get_florence_config(self) -> Florence2Config:
-        """
-        Build (and cache) the Florence2 transformer config that should back the VLM.
-        """
-        if self._florence_config_obj is None:
-            config_dict = dict(self.florence_config)
-            if "vision_config" not in config_dict or config_dict["vision_config"] is None:
-                raise ValueError("vision_config is required")
-
-            if "text_config" not in config_dict or config_dict["text_config"] is None:
-                raise ValueError("text_config is required")
-            self._florence_config_obj = Florence2Config(**config_dict)
-        return self._florence_config_obj
-
-    def validate_features(self) -> None:
-        if not self.image_features:
-            raise ValueError("XVLA requires at least one visual feature in the inputs.")
-        if self.use_proprio and self.robot_state_feature is None:
-            raise ValueError("`use_proprio=True` requires a proprioceptive state feature.")
-        if self.num_image_views is None:
-            self.num_image_views = len(self.image_features) + self.empty_cameras
-        else:
-            self.num_image_views = max(self.num_image_views, len(self.image_features) + self.empty_cameras)
-
-        if self.empty_cameras > 0:
-            height, width = (480, 640)
-            if self.resize_imgs_with_padding is not None:
-                height, width = self.resize_imgs_with_padding
-            for idx in range(self.empty_cameras):
-                key = f"{OBS_IMAGES}.empty_camera_{idx}"
-                if key not in self.input_features:
-                    self.input_features[key] = PolicyFeature(
-                        type=FeatureType.VISUAL,
-                        shape=(3, height, width),
-                    )
-
-    def get_optimizer_preset(self) -> XVLAAdamWConfig:
-        """Return the XVLA-specific optimizer with differential learning rates.
-
-        This optimizer applies:
-        - 1/10 LR for VLM parameters (stable optimization)
-        - Full LR for transformer/action head
-        - Configurable LR for soft-prompts (with optional warm-up)
-        """
-        return XVLAAdamWConfig(
-            lr=self.optimizer_lr,
-            betas=self.optimizer_betas,
-            eps=self.optimizer_eps,
-            weight_decay=self.optimizer_weight_decay,
-            grad_clip_norm=self.optimizer_grad_clip_norm,
-            soft_prompt_lr_scale=self.optimizer_soft_prompt_lr_scale,
-            soft_prompt_warmup_lr_scale=self.optimizer_soft_prompt_warmup_lr_scale,
-        )
-
-    def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
-        return CosineDecayWithWarmupSchedulerConfig(
-            peak_lr=self.optimizer_lr,
-            decay_lr=self.scheduler_decay_lr,
-            num_warmup_steps=self.scheduler_warmup_steps,
-            num_decay_steps=self.scheduler_decay_steps,
-        )
-
-    @property
-    def observation_delta_indices(self) -> list[int] | None:
-        return None
-
-    @property
-    def action_delta_indices(self) -> list[int]:
-        return list(range(self.chunk_size))
-
-    @property
-    def reward_delta_indices(self) -> list[int] | None:
-        return None

src/lerobot/policies/xvla/modeling_xvla.py

@@ -1,548 +0,0 @@
-#!/usr/bin/env python
-
-# ------------------------------------------------------------------------------
-# Copyright 2025 The HuggingFace Inc. team and 2toINF (https://github.com/2toINF)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ------------------------------------------------------------------------------
-
-from __future__ import annotations
-
-import builtins
-import logging
-import os
-from collections import deque
-from pathlib import Path
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import Tensor, nn
-
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.policies.pretrained import PreTrainedPolicy, T
-from lerobot.policies.utils import populate_queues
-from lerobot.utils.constants import ACTION, OBS_LANGUAGE_TOKENS, OBS_STATE
-
-from .action_hub import build_action_space
-from .configuration_florence2 import Florence2Config
-from .configuration_xvla import XVLAConfig
-from .modeling_florence2 import Florence2ForConditionalGeneration
-from .soft_transformer import SoftPromptedTransformer
-
-
-class XVLAModel(nn.Module):
-    """
-    XVLA backbone that stitches Florence-2 embeddings with the temporal/action transformer head.
-    """
-
-    def __init__(
-        self,
-        config: XVLAConfig,
-        florence_config: Florence2Config,
-        proprio_dim: int,
-    ) -> None:
-        super().__init__()
-        self.config = config
-        self.chunk_size: int = config.chunk_size
-        self.use_proprio: bool = config.use_proprio
-
-        # Build action space with auto-detection for "auto" mode
-        if config.action_mode.lower() == "auto":
-            # Auto-detect real action dim from config.action_feature
-            real_dim = (
-                config.action_feature.shape[-1]
-                if config.action_feature is not None
-                else config.max_action_dim
-            )
-            self.action_space = build_action_space(
-                config.action_mode.lower(),
-                real_dim=real_dim,
-                max_dim=config.max_action_dim,
-            )
-        else:
-            self.action_space = build_action_space(config.action_mode.lower())
-
-        self.dim_action = self.action_space.dim_action
-        self.dim_proprio = proprio_dim
-
-        self.vlm = Florence2ForConditionalGeneration(florence_config)
-        if hasattr(self.vlm, "language_model"):
-            lm = self.vlm.language_model
-            if hasattr(lm, "model") and hasattr(lm.model, "decoder"):
-                del lm.model.decoder
-            if hasattr(lm, "lm_head"):
-                del lm.lm_head
-
-        projection_dim = getattr(self.vlm.config, "projection_dim", None)
-        if projection_dim is None:
-            raise ValueError("Florence2 config must provide `projection_dim` for multimodal fusion.")
-
-        self.transformer = SoftPromptedTransformer(
-            hidden_size=config.hidden_size,
-            multi_modal_input_size=projection_dim,
-            depth=config.depth,
-            num_heads=config.num_heads,
-            mlp_ratio=config.mlp_ratio,
-            num_domains=config.num_domains,
-            dim_action=self.dim_action,
-            dim_propio=self.dim_proprio,
-            len_soft_prompts=config.len_soft_prompts,
-            dim_time=config.dim_time,
-            max_len_seq=config.max_len_seq,
-            use_hetero_proj=config.use_hetero_proj,
-        )
-
-        # Apply freezing based on config
-        self._apply_freezing()
-
-        # Apply dtype casting based on config
-        self._apply_dtype()
-
-    def _get_target_dtype(self) -> torch.dtype:
-        """Get the target dtype based on config."""
-        if self.config.dtype == "bfloat16":
-            return torch.bfloat16
-        return torch.float32
-
-    def _apply_dtype(self) -> None:
-        """
-        Apply dtype casting to model components based on config.
-        """
-        target_dtype = self._get_target_dtype()
-        self.to(dtype=target_dtype)
-
-    def _apply_freezing(self) -> None:
-        """
-        Freeze VLM vision and language encoders based on config options.
-        Keep only policy transformer and soft prompts trainable.
-        """
-        # Freeze vision encoder
-        if self.config.freeze_vision_encoder and hasattr(self.vlm, "vision_tower"):
-            for param in self.vlm.vision_tower.parameters():
-                param.requires_grad = False
-
-        # Freeze language encoder
-        if self.config.freeze_language_encoder and hasattr(self.vlm, "language_model"):
-            lm = self.vlm.language_model
-            # Freeze encoder
-            if hasattr(lm, "model") and hasattr(lm.model, "encoder"):
-                for param in lm.model.encoder.parameters():
-                    param.requires_grad = False
-            # Freeze shared embeddings
-            if hasattr(lm, "model") and hasattr(lm.model, "shared"):
-                for param in lm.model.shared.parameters():
-                    param.requires_grad = False
-
-        # Freeze or unfreeze policy transformer
-        if not self.config.train_policy_transformer:
-            for name, param in self.transformer.named_parameters():
-                if "soft_prompts" not in name:
-                    param.requires_grad = False
-
-        # Freeze or unfreeze soft prompts
-        if not self.config.train_soft_prompts and hasattr(self.transformer, "soft_prompt_hub"):
-            for param in self.transformer.soft_prompt_hub.parameters():
-                param.requires_grad = False
-
-    def forward_vlm(
-        self,
-        input_ids: torch.LongTensor,
-        pixel_values: torch.FloatTensor,
-        image_mask: torch.Tensor,
-    ) -> dict[str, torch.Tensor]:
-        """
-        Encode text and multi-view images via Florence2 encoder.
-        """
-        batch_size, num_views = pixel_values.shape[:2]
-        flat_mask = image_mask.view(-1).to(dtype=torch.bool)
-        flat_images = pixel_values.flatten(0, 1)
-        num_valid = int(flat_mask.sum().item())
-        if num_valid == 0:
-            raise ValueError("At least one image view must be valid per batch.")
-
-        valid_images = flat_images[flat_mask]
-        valid_feats = self.vlm._encode_image(valid_images)
-        tokens_per_view, hidden_dim = valid_feats.shape[1:]
-
-        image_features = valid_feats.new_zeros((batch_size * num_views, tokens_per_view, hidden_dim))
-        image_features[flat_mask] = valid_feats
-        image_features = image_features.view(batch_size, num_views, tokens_per_view, hidden_dim)
-        inputs_embeds = self.vlm.get_input_embeddings()(input_ids)
-        merged_embeds, attention_mask = self.vlm._merge_input_ids_with_image_features(
-            image_features[:, 0],
-            inputs_embeds,
-        )
-
-        enc_out = self.vlm.language_model.model.encoder(
-            attention_mask=attention_mask,
-            inputs_embeds=merged_embeds,
-        )[0]
-
-        aux_visual_inputs = image_features[:, 1:].reshape(batch_size, -1, hidden_dim)
-        return {"vlm_features": enc_out, "aux_visual_inputs": aux_visual_inputs}
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor,
-        image_input: torch.FloatTensor,
-        image_mask: torch.Tensor,
-        domain_id: torch.LongTensor,
-        proprio: torch.Tensor,
-        action: torch.Tensor,
-    ) -> dict[str, torch.Tensor]:
-        """
-        Forward pass for the XVLA model.
-        """
-        target_dtype = self._get_target_dtype()
-        image_input = image_input.to(dtype=target_dtype)
-        proprio = proprio.to(dtype=target_dtype)
-        action = action.to(dtype=target_dtype)
-
-        enc = self.forward_vlm(input_ids, image_input, image_mask)
-
-        batch_size = input_ids.shape[0]
-        t = (
-            torch.rand(1, device=input_ids.device, dtype=target_dtype)
-            + torch.arange(batch_size, device=input_ids.device, dtype=target_dtype) / batch_size
-        ) % (1 - 1e-5)
-
-        action_noisy = torch.randn_like(action) * t.view(-1, 1, 1) + action * (1 - t).view(-1, 1, 1)
-        proprio_m, action_noisy_m = self.action_space.preprocess(proprio, action_noisy)
-
-        pred_action = self.transformer(
-            domain_id=domain_id,
-            action_with_noise=action_noisy_m,
-            t=t,
-            proprio=proprio_m,
-            **enc,
-        )
-        return self.action_space.compute_loss(pred_action, action)
-
-    @torch.no_grad()
-    def generate_actions(
-        self,
-        input_ids: torch.LongTensor,
-        image_input: torch.FloatTensor,
-        image_mask: torch.Tensor,
-        domain_id: torch.LongTensor,
-        proprio: torch.Tensor,
-        steps: int,
-    ) -> torch.Tensor:
-        self.eval()
-
-        target_dtype = self._get_target_dtype()
-        image_input = image_input.to(dtype=target_dtype)
-        proprio = proprio.to(dtype=target_dtype)
-
-        enc = self.forward_vlm(input_ids, image_input, image_mask)
-
-        batch_size = input_ids.shape[0]
-        action_dim = self.dim_action
-
-        x1 = torch.randn(batch_size, self.chunk_size, action_dim, device=proprio.device, dtype=target_dtype)
-        action = torch.zeros_like(x1)
-
-        steps = max(1, int(steps))
-        for i in range(steps, 0, -1):
-            t = torch.full((batch_size,), i / steps, device=proprio.device, dtype=target_dtype)
-            x_t = x1 * t.view(-1, 1, 1) + action * (1 - t).view(-1, 1, 1)
-            proprio_m, x_t_m = self.action_space.preprocess(proprio, x_t)
-            action = self.transformer(
-                domain_id=domain_id,
-                action_with_noise=x_t_m,
-                proprio=proprio_m,
-                t=t,
-                **enc,
-            )
-        return self.action_space.postprocess(action)
-
-
-class XVLAPolicy(PreTrainedPolicy):
-    """LeRobot-compliant wrapper built around the XVLA model."""
-
-    config_class = XVLAConfig
-    name = "xvla"
-
-    def __init__(self, config: XVLAConfig):
-        super().__init__(config)
-        config.validate_features()
-        florence_config = config.get_florence_config()
-        proprio_dim = config.max_state_dim if config.use_proprio else 0
-        self.model = XVLAModel(config=config, florence_config=florence_config, proprio_dim=proprio_dim)
-        self.reset()
-
-    def reset(self) -> None:
-        self._queues = {
-            ACTION: deque(maxlen=self.config.n_action_steps),
-        }
-
-    def get_optim_params(self) -> dict:
-        """Return trainable named parameters for optimization.
-
-        Returns a dict of name -> param for all trainable parameters.
-        This enables the xvla-adamw optimizer to apply differential learning rates
-        based on parameter names (e.g., 1/10 LR for VLM components).
-        """
-        return dict(filter(lambda kv: kv[1].requires_grad, self.named_parameters()))
-
-    def _prepare_state(self, batch: dict[str, Tensor], batch_size: int, device: torch.device) -> Tensor:
-        if not self.config.use_proprio or OBS_STATE not in batch:
-            return torch.zeros(batch_size, 0, device=device)
-        state = batch[OBS_STATE]
-        if state.ndim > 2:
-            state = state[:, -1, :]
-        return pad_vector(state, self.model.dim_proprio)
-
-    def _prepare_images(self, batch: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
-        present_img_keys = [key for key in self.config.image_features if key in batch]
-        if len(present_img_keys) == 0:
-            raise ValueError(
-                "All image features are missing from the batch. "
-                f"Batch keys: {list(batch.keys())}, expected at least one of {list(self.config.image_features)}."
-            )
-
-        images = []
-        masks = []
-        for key in present_img_keys:
-            img = batch[key][:, -1] if batch[key].ndim == 5 else batch[key]
-            if self.config.resize_imgs_with_padding is not None:
-                img = resize_with_pad(img, *self.config.resize_imgs_with_padding)
-            images.append(img)
-            masks.append(torch.ones(img.size(0), dtype=torch.bool, device=img.device))
-
-        stacked_imgs = torch.stack(images, dim=1)
-        stacked_masks = torch.stack(masks, dim=1)
-
-        total_views = self.config.num_image_views or stacked_imgs.size(1)
-        total_views = max(total_views, stacked_imgs.size(1))
-        num_pad = total_views - stacked_imgs.size(1)
-        if num_pad > 0:
-            pad_shape = (stacked_imgs.size(0), num_pad, *stacked_imgs.shape[2:])
-            pad_imgs = stacked_imgs.new_zeros(pad_shape)
-            pad_masks = stacked_masks.new_zeros((stacked_masks.size(0), num_pad))
-            stacked_imgs = torch.cat([stacked_imgs, pad_imgs], dim=1)
-            stacked_masks = torch.cat([stacked_masks, pad_masks], dim=1)
-
-        return stacked_imgs, stacked_masks
-
-    def _get_domain_id(self, batch: dict[str, Tensor], batch_size: int, device: torch.device) -> Tensor:
-        candidate = None
-        if self.config.domain_feature_key and self.config.domain_feature_key in batch:
-            candidate = batch[self.config.domain_feature_key]
-        elif "domain_id" in batch:
-            candidate = batch["domain_id"]
-
-        if candidate is None:
-            return torch.zeros(batch_size, dtype=torch.long, device=device)
-
-        if not isinstance(candidate, torch.Tensor):
-            candidate = torch.as_tensor(candidate, device=device)
-        else:
-            candidate = candidate.to(device=device)
-
-        if candidate.ndim == 0:
-            candidate = candidate.expand(batch_size)
-        if candidate.ndim > 1:
-            candidate = candidate.view(candidate.shape[0], -1)[:, 0]
-        if candidate.shape[0] != batch_size:
-            candidate = candidate.expand(batch_size)
-        return candidate.to(dtype=torch.long)
-
-    def _prepare_action_targets(self, batch: dict[str, Tensor]) -> Tensor:
-        if ACTION not in batch:
-            raise ValueError("Batch is missing action targets required for training.")
-        actions = batch[ACTION]
-        if actions.ndim == 2:
-            actions = actions.unsqueeze(1)
-        actions = pad_tensor_along_dim(actions, self.config.chunk_size, dim=1)
-        if actions.shape[-1] != self.model.dim_action:
-            actions = pad_vector(actions, self.model.dim_action)
-        return actions
-
-    def _build_model_inputs(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        input_ids = batch[OBS_LANGUAGE_TOKENS]
-        batch_size = input_ids.shape[0]
-        images, image_mask = self._prepare_images(batch)
-        domain_id = self._get_domain_id(batch, batch_size, images.device)
-        proprio = self._prepare_state(batch, batch_size, images.device)
-        return {
-            "input_ids": input_ids,
-            "image_input": images,
-            "image_mask": image_mask,
-            "domain_id": domain_id,
-            "proprio": proprio,
-        }
-
-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
-        inputs = self._build_model_inputs(batch)
-        targets = self._prepare_action_targets(batch)
-        losses = self.model(action=targets, **inputs)
-        total_loss = sum(losses.values())
-
-        log_dict = {k: v.detach().item() for k, v in losses.items()}
-        log_dict["loss"] = total_loss.detach().item()
-        return total_loss, log_dict
-
-    def _get_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
-        inputs = self._build_model_inputs(batch)
-        actions = self.model.generate_actions(**inputs, steps=self.config.num_denoising_steps)
-        return actions
-
-    @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:  # noqa: ARG002
-        self.eval()
-        self._queues = populate_queues(self._queues, batch, exclude_keys=[ACTION])
-        return self._get_action_chunk(batch)
-
-    @torch.no_grad()
-    def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:  # noqa: ARG002
-        self.eval()
-        self._queues = populate_queues(self._queues, batch, exclude_keys=[ACTION])
-
-        if len(self._queues[ACTION]) == 0:
-            actions = self._get_action_chunk(batch)
-            self._queues[ACTION].extend(actions.transpose(0, 1)[: self.config.n_action_steps])
-
-        return self._queues[ACTION].popleft()
-
-    @classmethod
-    def from_pretrained(
-        cls: builtins.type[T],
-        pretrained_name_or_path: str | Path,
-        *,
-        config: PreTrainedConfig | None = None,
-        force_download: bool = False,
-        resume_download: bool | None = None,
-        proxies: dict | None = None,
-        token: str | bool | None = None,
-        cache_dir: str | Path | None = None,
-        local_files_only: bool = False,
-        revision: str | None = None,
-        strict: bool = False,
-        **kwargs,
-    ):
-        """
-        Loads XVLA model weights with:
-        - automatic prefix 'model.' added to all keys
-        - skip list for layers that should remain randomly initialized
-        """
-        import safetensors.torch
-
-        # step 1: load config
-        # TODO: jadechoghari, fix this
-        if config is None:
-            config = PreTrainedConfig.from_pretrained(
-                pretrained_name_or_path=pretrained_name_or_path,
-                force_download=force_download,
-                resume_download=resume_download,
-                proxies=proxies,
-                token=token,
-                cache_dir=cache_dir,
-                local_files_only=local_files_only,
-                revision=revision,
-                **kwargs,
-            )
-
-        model_id = str(pretrained_name_or_path)
-        instance = cls(config, **kwargs)
-        # step 2: locate model.safetensors
-        if os.path.isdir(model_id):
-            logging.info("Loading weights from local directory")
-            model_file = os.path.join(model_id, "model.safetensors")
-        else:
-            try:
-                from huggingface_hub import hf_hub_download
-                from huggingface_hub.utils import HfHubHTTPError
-
-                model_file = hf_hub_download(
-                    repo_id=model_id,
-                    filename="model.safetensors",
-                    revision=revision,
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    resume_download=resume_download,
-                    token=token,
-                    local_files_only=local_files_only,
-                )
-            except HfHubHTTPError as e:
-                raise FileNotFoundError(f"model.safetensors not found on the Hub at {model_id}") from e
-
-        logging.info(f"Loading checkpoint from {model_file}")
-        # step 3: load state dict
-        state_dict = safetensors.torch.load_file(model_file)
-        encoder_key = "model.vlm.language_model.model.encoder.embed_tokens.weight"
-        shared_key = "model.vlm.language_model.model.shared.weight"
-        if encoder_key in state_dict:
-            state_dict[shared_key] = state_dict[encoder_key]
-            # or deepcopy
-        # step 4: load into instance
-        instance.load_state_dict(state_dict, strict=True)
-        logging.info("Loaded XVLA checkpoint")
-        # step 5: finalize
-        # Reapply dtype after loading state dict
-        instance.model._apply_dtype()
-        instance.to(config.device)
-        instance.eval()
-        return instance
-
-
-def resize_with_pad(img: torch.Tensor, height: int, width: int, pad_value: float = 0.0) -> torch.Tensor:
-    if img.ndim != 4:
-        raise ValueError(f"(b,c,h,w) expected, but got {img.shape}")
-
-    current_height, current_width = img.shape[2:]
-    if current_height == height and current_width == width:
-        return img
-
-    ratio = max(current_width / width, current_height / height)
-    resized_height = int(current_height / ratio)
-    resized_width = int(current_width / ratio)
-    resized_img = F.interpolate(
-        img, size=(resized_height, resized_width), mode="bilinear", align_corners=False
-    )
-
-    pad_height = max(0, height - resized_height)
-    pad_width = max(0, width - resized_width)
-    padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value)
-    return padded_img
-
-
-def pad_vector(vector: Tensor, new_dim: int) -> Tensor:
-    if vector.shape[-1] == new_dim:
-        return vector
-    if new_dim == 0:
-        shape = list(vector.shape)
-        shape[-1] = 0
-        return vector.new_zeros(*shape)
-    shape = list(vector.shape)
-    current_dim = shape[-1]
-    shape[-1] = new_dim
-    new_vector = vector.new_zeros(*shape)
-    length = min(current_dim, new_dim)
-    new_vector[..., :length] = vector[..., :length]
-    return new_vector
-
-
-def pad_tensor_along_dim(tensor: Tensor, target_len: int, dim: int = 1) -> Tensor:
-    current_len = tensor.size(dim)
-    if current_len == target_len:
-        return tensor
-    if current_len > target_len:
-        slices = [slice(None)] * tensor.dim()
-        slices[dim] = slice(0, target_len)
-        return tensor[tuple(slices)]
-    pad_shape = list(tensor.shape)
-    pad_shape[dim] = target_len - current_len
-    pad_tensor = tensor.new_zeros(pad_shape)
-    return torch.cat([tensor, pad_tensor], dim=dim)

src/lerobot/policies/xvla/processor_xvla.py

@@ -1,554 +0,0 @@
-# ------------------------------------------------------------------------------
-# Copyright 2025 The HuggingFace Inc. team and 2toINF (https://github.com/2toINF)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ------------------------------------------------------------------------------
-
-from dataclasses import dataclass
-from typing import Any
-
-import numpy as np
-import torch
-
-from lerobot.configs.types import PipelineFeatureType, PolicyFeature
-from lerobot.datasets.factory import IMAGENET_STATS
-from lerobot.policies.xvla.configuration_xvla import XVLAConfig
-from lerobot.policies.xvla.utils import rotate6d_to_axis_angle
-from lerobot.processor import (
-    AddBatchDimensionProcessorStep,
-    DeviceProcessorStep,
-    NormalizerProcessorStep,
-    ObservationProcessorStep,
-    PolicyAction,
-    PolicyProcessorPipeline,
-    ProcessorStep,
-    ProcessorStepRegistry,
-    RenameObservationsProcessorStep,
-    TokenizerProcessorStep,
-    UnnormalizerProcessorStep,
-)
-from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
-from lerobot.processor.core import EnvTransition, TransitionKey
-from lerobot.utils.constants import (
-    OBS_IMAGES,
-    OBS_STATE,
-    POLICY_POSTPROCESSOR_DEFAULT_NAME,
-    POLICY_PREPROCESSOR_DEFAULT_NAME,
-)
-
-
-def make_xvla_pre_post_processors(
-    config: XVLAConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    """
-    Build the LeRobot processor pipelines for XVLA.
-    """
-
-    features = {**config.input_features, **config.output_features}
-    input_steps = [
-        RenameObservationsProcessorStep(rename_map={}),
-        AddBatchDimensionProcessorStep(),
-        TokenizerProcessorStep(
-            tokenizer_name=config.tokenizer_name,
-            max_length=config.tokenizer_max_length,
-            padding=config.pad_language_to,
-            padding_side=config.tokenizer_padding_side,
-        ),
-        XVLAImageToFloatProcessorStep(),
-        XVLAImageNetNormalizeProcessorStep(),
-        XVLAAddDomainIdProcessorStep(),
-        DeviceProcessorStep(device=config.device),
-        NormalizerProcessorStep(
-            features=features, norm_map=config.normalization_mapping, stats=dataset_stats
-        ),
-    ]
-    output_steps = [
-        UnnormalizerProcessorStep(
-            features=config.output_features,
-            norm_map=config.normalization_mapping,
-            stats=dataset_stats,
-        ),
-        DeviceProcessorStep(device="cpu"),
-    ]
-
-    return (
-        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
-            steps=input_steps,
-            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
-        ),
-        PolicyProcessorPipeline[PolicyAction, PolicyAction](
-            steps=output_steps,
-            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
-            to_transition=policy_action_to_transition,
-            to_output=transition_to_policy_action,
-        ),
-    )
-
-
-# Custom XVLA processor steps
-@dataclass
-class LiberoProcessorStep(ObservationProcessorStep):
-    """
-    Processes LIBERO observations into the LeRobot format.
-
-    This step handles the specific observation structure from LIBERO environments,
-    which includes nested robot_state dictionaries and image observations.
-
-    **State Processing:**
-    -   Processes the `robot_state` dictionary which contains nested end-effector,
-        gripper, and joint information.
-    -   Extracts and concatenates:
-        - End-effector position (3D)
-        - End-effector quaternion converted to axis-angle (3D)
-        - Gripper joint positions (2D)
-    -   Maps the concatenated state to `"observation.state"`.
-
-    **Image Processing:**
-    -   Rotates images by 180 degrees by flipping both height and width dimensions.
-    -   This accounts for the HuggingFaceVLA/libero camera orientation convention.
-    """
-
-    def _process_observation(self, observation):
-        """
-        Processes both image and robot_state observations from LIBERO.
-        """
-        processed_obs = observation.copy()
-        for key in list(processed_obs.keys()):
-            if key.startswith(f"{OBS_IMAGES}."):
-                img = processed_obs[key]
-
-                if key == f"{OBS_IMAGES}.image":
-                    # Flip both H and W
-                    img = torch.flip(img, dims=[2, 3])
-
-                processed_obs[key] = img
-        # Process robot_state into a flat state vector
-        if "observation.robot_state" in processed_obs:
-            robot_state = processed_obs.pop("observation.robot_state")
-
-            # Extract components
-            eef_pos = robot_state["eef"]["pos"]  # (B, 3,)
-            eef_mat = robot_state["eef"]["mat"]  # (B, 3, 3)
-            eef_rot6d = self._mat_to_rotate6d(eef_mat)  # (B, 6)
-
-            extra = torch.zeros((eef_pos.shape[0], 1), dtype=torch.float32, device=eef_pos.device)
-
-            proprio_state = torch.cat((eef_pos, eef_rot6d, extra), dim=-1)  # (B, 10)
-            state = torch.cat((proprio_state, torch.zeros_like(proprio_state)), dim=-1)  # (B, 20)
-            # ensure float32
-            state = state.float()
-            if state.dim() == 1:
-                state = state.unsqueeze(0)
-
-            processed_obs[OBS_STATE] = state
-        return processed_obs
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        """
-        Transforms feature keys from the LIBERO format to the LeRobot standard.
-        """
-        new_features: dict[PipelineFeatureType, dict[str, PolicyFeature]] = {}
-
-        # copy over non-STATE features
-        for ft, feats in features.items():
-            if ft != PipelineFeatureType.STATE:
-                new_features[ft] = feats.copy()
-
-        # rebuild STATE features
-        state_feats = {}
-
-        # add our new flattened state
-        state_feats["observation.state"] = PolicyFeature(
-            key="observation.state",
-            shape=(20,),
-            dtype="float32",
-        )
-
-        new_features[PipelineFeatureType.STATE] = state_feats
-
-        return new_features
-
-    def _mat_to_rotate6d(self, rot_mats: torch.Tensor) -> torch.Tensor:
-        """
-        Convert batched rotation matrices (B, 3, 3) into 6D rotation representation (B, 6).
-
-        Args:
-            rot_mats (Tensor): Rotation matrices of shape (B, 3, 3)
-
-        Returns:
-            Tensor: 6D rotation representation, shape (B, 6)
-
-        Raises:
-            TypeError: if input is not a torch tensor
-            ValueError: if shape is not (B, 3, 3)
-        """
-
-        if not isinstance(rot_mats, torch.Tensor):
-            raise TypeError(f"mat_to_rot6d expects a torch.Tensor, got {type(rot_mats)}")
-
-        if rot_mats.ndim != 3 or rot_mats.shape[1:] != (3, 3):
-            raise ValueError(f"mat_to_rot6d expects shape (B, 3, 3), got {tuple(rot_mats.shape)}")
-
-        rot_mats = rot_mats.to(torch.float32)
-
-        col1 = rot_mats[:, :3, 0]  # (B, 3)
-        col2 = rot_mats[:, :3, 1]  # (B, 3)
-
-        rot6d = torch.cat([col1, col2], dim=-1)  # (B, 6)
-
-        return rot6d
-
-    def observation(self, observation):
-        return self._process_observation(observation)
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="xvla_image_scale")
-class XVLAImageScaleProcessorStep(ProcessorStep):
-    """Scale image observations by 255 to convert from [0, 1] to [0, 255] range.
-
-    This processor step multiplies all image observations by 255, which is required
-    for XVLA models that expect images in uint8-like range.
-
-    Args:
-        image_keys: List of observation keys that contain images to scale.
-                   If None, will automatically detect keys starting with "observation.images."
-    """
-
-    image_keys: list[str] | None = None
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        """Scale image observations by 255."""
-        new_transition = transition.copy()
-        obs = new_transition.get(TransitionKey.OBSERVATION, {})
-        if obs is None:
-            return new_transition
-
-        # Make a copy of observations to avoid modifying the original
-        obs = obs.copy()
-
-        # Determine which keys to scale
-        keys_to_scale = self.image_keys
-        if keys_to_scale is None:
-            # Auto-detect image keys
-            keys_to_scale = [k for k in obs if k.startswith("observation.images.")]
-
-        # Scale each image
-        for key in keys_to_scale:
-            if key in obs and isinstance(obs[key], torch.Tensor):
-                obs[key] = obs[key] * 255
-
-        new_transition[TransitionKey.OBSERVATION] = obs
-        return new_transition
-
-    def transform_features(self, features):
-        """Image scaling doesn't change feature structure."""
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        """Return serializable configuration."""
-        return {
-            "image_keys": self.image_keys,
-        }
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="xvla_image_to_float")
-class XVLAImageToFloatProcessorStep(ProcessorStep):
-    """Convert image observations from [0, 255] to [0, 1] range.
-
-    This processor step divides image observations by 255 to convert from uint8-like
-    range [0, 255] to float range [0, 1]. This is typically used when loading images
-    that are stored as uint8 values.
-
-    Args:
-        image_keys: List of observation keys that contain images to convert.
-                   If None, will automatically detect keys starting with "observation.images."
-        validate_range: If True, validates that input values are in [0, 255] range (default: True)
-
-    Raises:
-        ValueError: If validate_range is True and image values are not in [0, 255] range.
-    """
-
-    image_keys: list[str] | None = None
-    validate_range: bool = True
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        """Convert image observations from [0, 255] to [0, 1]."""
-        new_transition = transition.copy()
-        obs = new_transition.get(TransitionKey.OBSERVATION, {})
-        if obs is None:
-            return new_transition
-
-        # Make a copy of observations to avoid modifying the original
-        obs = obs.copy()
-
-        # Determine which keys to convert
-        keys_to_convert = self.image_keys
-        if keys_to_convert is None:
-            # Auto-detect image keys
-            keys_to_convert = [k for k in obs if k.startswith("observation.images.")]
-
-        # Convert each image
-        for key in keys_to_convert:
-            if key in obs and isinstance(obs[key], torch.Tensor):
-                tensor = obs[key]
-
-                min_val = tensor.min().item()
-                max_val = tensor.max().item()
-
-                if max_val <= 1.0:
-                    obs[key] = tensor.float()  # ensure float dtype, but no division
-                    continue
-                # Validate that values are in [0, 255] range if requested
-                if self.validate_range and (min_val < 0.0 or max_val > 255.0):
-                    raise ValueError(
-                        f"Image '{key}' has values outside [0, 255] range: "
-                        f"min={min_val:.4f}, max={max_val:.4f}. "
-                        f"Cannot convert to [0, 1] range."
-                    )
-
-                # Convert to float and divide by 255
-                obs[key] = tensor.float() / 255.0
-
-        new_transition[TransitionKey.OBSERVATION] = obs
-        return new_transition
-
-    def transform_features(self, features):
-        """Image conversion doesn't change feature structure."""
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        """Return serializable configuration."""
-        return {
-            "image_keys": self.image_keys,
-            "validate_range": self.validate_range,
-        }
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="xvla_imagenet_normalize")
-class XVLAImageNetNormalizeProcessorStep(ProcessorStep):
-    """Normalize image observations using ImageNet statistics.
-
-    This processor step applies ImageNet normalization (mean and std) to image observations.
-    It validates that input values are in the [0, 1] range before normalizing.
-
-    The normalization formula is: (image - mean) / std
-
-    Args:
-        image_keys: List of observation keys that contain images to normalize.
-                   If None, will automatically detect keys starting with "observation.images."
-
-    Raises:
-        ValueError: If image values are not in the [0, 1] range.
-    """
-
-    image_keys: list[str] | None = None
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        """Normalize image observations using ImageNet statistics."""
-        new_transition = transition.copy()
-        obs = new_transition.get(TransitionKey.OBSERVATION, {})
-        if obs is None:
-            return new_transition
-
-        # Make a copy of observations to avoid modifying the original
-        obs = obs.copy()
-
-        # Determine which keys to normalize
-        keys_to_normalize = self.image_keys
-        if keys_to_normalize is None:
-            # Auto-detect image keys
-            keys_to_normalize = [k for k in obs if k.startswith("observation.images.")]
-
-        # Normalize each image
-        for key in keys_to_normalize:
-            if key in obs and isinstance(obs[key], torch.Tensor):
-                tensor = obs[key]
-
-                # Validate that values are in [0, 1] range
-                min_val = tensor.min().item()
-                max_val = tensor.max().item()
-                if min_val < 0.0 or max_val > 1.0:
-                    raise ValueError(
-                        f"Image '{key}' has values outside [0, 1] range: "
-                        f"min={min_val:.4f}, max={max_val:.4f}. "
-                        f"ImageNet normalization requires input values in [0, 1]."
-                    )
-
-                # Apply ImageNet normalization
-                mean = torch.tensor(IMAGENET_STATS["mean"], device=tensor.device, dtype=tensor.dtype)
-                std = torch.tensor(IMAGENET_STATS["std"], device=tensor.device, dtype=tensor.dtype)
-
-                # Expand mean/std to match tensor dims (e.g., BCHW or BNCHW)
-                while mean.dim() < tensor.dim():
-                    mean = mean.unsqueeze(0)
-                    std = std.unsqueeze(0)
-
-                # Normalize: (image - mean) / std
-                obs[key] = (tensor - mean) / std
-
-        new_transition[TransitionKey.OBSERVATION] = obs
-        return new_transition
-
-    def transform_features(self, features):
-        """ImageNet normalization doesn't change feature structure."""
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        """Return serializable configuration."""
-        return {
-            "image_keys": self.image_keys,
-        }
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="xvla_add_domain_id")
-class XVLAAddDomainIdProcessorStep(ProcessorStep):
-    """Add domain_id to complementary data.
-
-    This processor step adds a domain_id tensor to the complementary data,
-    which is used by XVLA to identify different robot embodiments or task domains.
-
-    Args:
-        domain_id: The domain ID to add (default: 3)
-    """
-
-    domain_id: int = 0
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        """Add domain_id to complementary data."""
-        new_transition = transition.copy()
-        comp = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
-        comp = {} if comp is None else comp.copy()
-
-        # Infer batch size from observation tensors
-        obs = new_transition.get(TransitionKey.OBSERVATION, {})
-        batch_size = 1
-        if obs:
-            for v in obs.values():
-                if isinstance(v, torch.Tensor):
-                    batch_size = v.shape[0]
-                    break
-
-        # Add domain_id tensor
-        comp["domain_id"] = torch.tensor([int(self.domain_id)] * batch_size, dtype=torch.long)
-
-        new_transition[TransitionKey.COMPLEMENTARY_DATA] = comp
-        return new_transition
-
-    def transform_features(self, features):
-        """Domain ID addition doesn't change feature structure."""
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        """Return serializable configuration."""
-        return {
-            "domain_id": self.domain_id,
-        }
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="xvla_rotation_6d_to_axis_angle")
-class XVLARotation6DToAxisAngleProcessorStep(ProcessorStep):
-    """Convert 6D rotation representation to axis-angle and reorganize action dimensions.
-
-    This processor step takes actions with 6D rotation representation and converts them to
-    axis-angle representation, reorganizing the action dimensions as:
-    - action[:, :3] -> target_eef (end-effector position)
-    - action[:, 3:9] -> 6D rotation (converted to axis-angle, 3D)
-    - action[:, 9:10] -> gripper action
-
-    Final output: [target_eef (3), axis_angle (3), gripper (1)] = 7D action
-
-    Args:
-        expected_action_dim: Expected input action dimension (default: 10, supports 6D rotation + extras)
-    """
-
-    expected_action_dim: int = 10
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        """Convert 6D rotation to axis-angle in action."""
-        new_transition = transition.copy()
-        action = new_transition.get(TransitionKey.ACTION)
-
-        if action is None or not isinstance(action, torch.Tensor):
-            return new_transition
-
-        # Convert to numpy for processing
-        device = action.device
-        dtype = action.dtype
-        action_np = action.cpu().numpy()
-
-        # Extract components
-        # action shape: (B, D) where D >= 10
-        target_eef = action_np[:, :3]  # (B, 3)
-        rotation_6d = action_np[:, 3:9]  # (B, 6)
-        target_act = action_np[:, 9:10]  # (B, 1)
-
-        # Convert 6D rotation to axis-angle
-        target_axis = rotate6d_to_axis_angle(rotation_6d)  # (B, 3)
-
-        # Concatenate: [eef (3), axis_angle (3), gripper (1)] = 7D
-        action_np = np.concatenate([target_eef, target_axis, target_act], axis=-1)
-
-        # Convert gripper action to -1 or 1
-        action_np[:, -1] = np.where(action_np[:, -1] > 0.5, 1.0, -1.0)
-
-        # Convert back to tensor
-        action = torch.from_numpy(action_np).to(device=device, dtype=dtype)
-
-        new_transition[TransitionKey.ACTION] = action
-        return new_transition
-
-    def transform_features(self, features):
-        """Rotation conversion changes action dimension from 10 to 7."""
-        # Note: This is a simplified version. In practice, you might want to
-        # update the action feature shape in the features dict.
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        """Return serializable configuration."""
-        return {
-            "expected_action_dim": self.expected_action_dim,
-        }
-
-
-def make_xvla_libero_pre_post_processors() -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    """
-    Build the LeRobot processor pipelines for XVLA with LIBERO environment.
-    """
-    pre_processor_steps: list[ProcessorStep] = []
-    post_processor_steps: list[ProcessorStep] = []
-    pre_processor_steps.extend(
-        [LiberoProcessorStep(), XVLAImageNetNormalizeProcessorStep(), XVLAAddDomainIdProcessorStep()]
-    )
-    post_processor_steps.extend([XVLARotation6DToAxisAngleProcessorStep()])
-    return (
-        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
-            steps=pre_processor_steps,
-        ),
-        PolicyProcessorPipeline[PolicyAction, PolicyAction](
-            steps=post_processor_steps,
-        ),
-    )

src/lerobot/policies/xvla/soft_transformer.py

@@ -1,415 +0,0 @@
-# ------------------------------------------------------------------------------
-# Copyright 2025 2toINF (https://github.com/2toINF)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ------------------------------------------------------------------------------
-
-from __future__ import annotations
-
-import math
-from collections.abc import Iterable
-from functools import partial
-from typing import Final
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as functional
-
-# ------------------------------- Small utils ----------------------------------
-
-
-def _to_2tuple(x) -> tuple:
-    """Minimal replacement for timm.layers.to_2tuple."""
-    if isinstance(x, Iterable) and not isinstance(x, (str, bytes)):
-        t = tuple(x)
-        return (t[0], t[1]) if len(t) >= 2 else (t[0], t[0])
-    return (x, x)
-
-
-def _has_sdp_attention() -> bool:
-    """Check if we can use PyTorch fused scaled_dot_product_attention."""
-    return hasattr(functional, "scaled_dot_product_attention")
-
-
-# ---------------------------------- MLP --------------------------------------
-
-
-class Mlp(nn.Module):
-    """
-    MLP used in ViT-style blocks.
-
-    Supports Linear or 1x1 Conv 'linear_layer' for token/channel mixing.
-    """
-
-    def __init__(
-        self,
-        in_features: int,
-        hidden_features: int | None = None,
-        out_features: int | None = None,
-        norm_layer: type[nn.Module] | None = None,
-        bias: bool | tuple[bool, bool] = True,
-        drop: float | tuple[float, float] = 0.0,
-        use_conv: bool = False,
-    ) -> None:
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        bias = _to_2tuple(bias)
-        drop_probs = _to_2tuple(drop)
-        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
-
-        self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
-        self.act = nn.GELU(approximate="tanh")
-        self.drop1 = nn.Dropout(drop_probs[0])
-        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
-        self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
-        self.drop2 = nn.Dropout(drop_probs[1])
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        # Expect [B, T, C] for Linear variant; caller is responsible for shapes.
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop1(x)
-        x = self.norm(x)
-        x = self.fc2(x)
-        x = self.drop2(x)
-        return x
-
-
-# -------------------------------- Attention ----------------------------------
-
-
-class Attention(nn.Module):
-    """
-    Multi-Head Self-Attention with optional fused SDPA fallback.
-
-    If PyTorch provides `scaled_dot_product_attention`, it will be used
-    (usually faster and more stable); otherwise we use a manual implementation.
-    """
-
-    fused_attn: Final[bool]
-
-    def __init__(
-        self,
-        dim: int,
-        num_heads: int = 8,
-        qkv_bias: bool = False,
-        qk_norm: bool = False,
-        attn_drop: float = 0.0,
-        proj_drop: float = 0.0,
-        norm_layer: type[nn.Module] = nn.LayerNorm,
-    ) -> None:
-        super().__init__()
-        assert dim % num_heads == 0, "dim should be divisible by num_heads"
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim**-0.5
-        self.fused_attn = _has_sdp_attention()
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Parameters
-        ----------
-        x : Tensor, shape [batch_size, seq_len, channels]
-            Input sequence.
-
-        Returns
-        -------
-        Tensor, shape [batch_size, seq_len, channels]
-            Output sequence after MHSA + projection.
-        """
-        batch_size, seq_len, channels = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(batch_size, seq_len, 3, self.num_heads, self.head_dim)
-            .permute(2, 0, 3, 1, 4)  # 3 x [batch_size, num_heads, seq_len, head_dim]
-        )
-        q, k, v = qkv.unbind(0)  # each: [batch_size, num_heads, seq_len, head_dim]
-        q, k = self.q_norm(q), self.k_norm(k)
-
-        if self.fused_attn:
-            x = functional.scaled_dot_product_attention(
-                q,
-                k,
-                v,
-                dropout_p=self.attn_drop.p if self.training else 0.0,
-            )  # [batch_size, num_heads, seq_len, head_dim]
-        else:
-            q = q * self.scale
-            attn = q @ k.transpose(-2, -1)  # [batch_size, num_heads, seq_len, seq_len]
-            attn = attn.softmax(dim=-1)
-            attn = self.attn_drop(attn)
-            x = attn @ v  # [batch_size, num_heads, seq_len, head_dim]
-
-        x = x.transpose(1, 2).reshape(batch_size, seq_len, channels)  # [batch_size, seq_len, channels]
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-# ------------------------------- Utilities -----------------------------------
-
-
-def basic_init(module: nn.Module) -> None:
-    """
-    Apply a basic initialization scheme to Linear layers.
-
-    - Weight: Xavier uniform initialization.
-    - Bias: Set to zero.
-    """
-    if isinstance(module, nn.Linear):
-        nn.init.xavier_uniform_(module.weight)
-        if module.bias is not None:
-            nn.init.constant_(module.bias, 0.0)
-
-
-def timestep_embedding(t: torch.Tensor, dim: int, max_period: int = 100) -> torch.Tensor:
-    """
-    Create sinusoidal timestep embeddings.
-
-    Parameters
-    ----------
-    t : torch.Tensor
-        Shape [B]. Each element is a timestep index, may be fractional.
-    dim : int
-        Dimensionality of the output embedding.
-    max_period : int, default=100
-        Controls the minimum frequency of the sinusoids.
-
-    Returns
-    -------
-    torch.Tensor
-        Shape [B, dim]. Sinusoidal embeddings.
-    """
-    half = dim // 2
-    freqs = torch.exp(
-        -math.log(max_period) * torch.arange(start=0, end=half, dtype=t.dtype, device=t.device) / half
-    )
-    args = t[:, None] * freqs[None]
-    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-    if dim % 2 == 1:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    return embedding
-
-
-# ------------------------------- Core Layers ----------------------------------
-
-
-class DomainAwareLinear(nn.Module):
-    """
-    Linear layer with domain-conditioned parameters (per-sample).
-
-    Each domain has its own weight and bias vectors, stored in embeddings.
-    """
-
-    def __init__(self, input_size: int, output_size: int, num_domains: int = 20) -> None:
-        super().__init__()
-        self.input_size = input_size
-        self.output_size = output_size
-        self.fc = nn.Embedding(num_domains, output_size * input_size)
-        self.bias = nn.Embedding(num_domains, output_size)
-        nn.init.xavier_uniform_(self.fc.weight)
-        nn.init.zeros_(self.bias.weight)
-
-    def forward(self, x: torch.Tensor, domain_id: torch.LongTensor) -> torch.Tensor:
-        """
-        Parameters
-        ----------
-        x : Tensor
-            [B, I] or [B, T, I]
-        domain_id : LongTensor
-            [B], domain indices.
-
-        Returns
-        -------
-        Tensor
-            [batch_size, output_size] or [batch_size, seq_len, output_size]
-        """
-        batch_size = domain_id.shape[0]
-        squeeze_seq = False
-        if x.dim() == 2:
-            x = x.unsqueeze(1)
-            squeeze_seq = True
-        weight = self.fc(domain_id).view(batch_size, self.input_size, self.output_size)
-        bias = self.bias(domain_id).view(batch_size, self.output_size)
-        y = torch.matmul(x, weight) + bias.view(batch_size, 1, self.output_size)
-        if squeeze_seq:
-            y = y.squeeze(1)
-        return y
-
-
-class TransformerBlock(nn.Module):
-    """
-    Standard Transformer block (pre-LN): LN → MHSA → residual, LN → MLP → residual.
-    """
-
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0) -> None:
-        super().__init__()
-        self.norm1 = nn.LayerNorm(hidden_size)
-        self.norm2 = nn.LayerNorm(hidden_size)
-        self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, attn_drop=0.1)
-        self.mlp = Mlp(
-            in_features=hidden_size,
-            hidden_features=int(hidden_size * mlp_ratio),
-            drop=0.1,
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Parameters
-        ----------
-        x : Tensor, [B, T, H]
-
-        Returns
-        -------
-        Tensor, [B, T, H]
-        """
-        x = x + self.attn(self.norm1(x))
-        x = x + self.mlp(self.norm2(x))
-        return x
-
-
-# --------------------------- Main Model ---------------------------------------
-
-
-class SoftPromptedTransformer(nn.Module):
-    """
-    Multi-modal, domain-aware Transformer with optional soft prompts.
-
-    See parameter and forward I/O descriptions inside the docstrings.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int = 768,
-        multi_modal_input_size: int = 768,
-        depth: int = 24,
-        num_heads: int = 16,
-        mlp_ratio: float = 4.0,
-        num_domains: int = 20,
-        dim_action: int = 20,
-        dim_propio: int = 20,
-        dim_time: int = 32,
-        len_soft_prompts: int = 32,
-        max_len_seq: int = 512,
-        use_hetero_proj: bool = False,
-    ) -> None:
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.dim_action = dim_action
-        self.dim_time = dim_time
-        self.len_soft_prompts = len_soft_prompts
-        self.use_hetero_proj = use_hetero_proj
-
-        self.blocks = nn.ModuleList(
-            [TransformerBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)]
-        )
-
-        if use_hetero_proj:
-            self.vlm_proj = DomainAwareLinear(multi_modal_input_size, hidden_size, num_domains=num_domains)
-            self.aux_visual_proj = DomainAwareLinear(
-                multi_modal_input_size, hidden_size, num_domains=num_domains
-            )
-        else:
-            self.vlm_proj = nn.Linear(multi_modal_input_size, hidden_size)
-            self.aux_visual_proj = nn.Linear(multi_modal_input_size, hidden_size)
-
-        self.pos_emb = nn.Parameter(torch.zeros(1, max_len_seq, hidden_size), requires_grad=True)
-        nn.init.normal_(self.pos_emb, std=0.02)
-
-        self.norm = nn.LayerNorm(hidden_size)
-        self.action_encoder = DomainAwareLinear(
-            dim_action + dim_time + dim_propio, hidden_size, num_domains=num_domains
-        )
-        self.action_decoder = DomainAwareLinear(hidden_size, dim_action, num_domains=num_domains)
-
-        if len_soft_prompts > 0:
-            self.soft_prompt_hub = nn.Embedding(num_domains, len_soft_prompts * hidden_size)
-            nn.init.normal_(self.soft_prompt_hub.weight, std=0.02)
-
-        self.apply(basic_init)
-
-    def forward(
-        self,
-        domain_id: torch.LongTensor,
-        vlm_features: torch.Tensor,
-        aux_visual_inputs: torch.Tensor,
-        action_with_noise: torch.Tensor,
-        proprio: torch.Tensor,
-        t: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Forward pass.
-
-        Inputs
-        ------
-        domain_id : [B]
-        vlm_features : [B, T_vlm, D]
-        aux_visual_inputs : [B, T_aux, D]
-        action_with_noise : [B, T_action, dim_action]
-        proprio : [B, dim_propio]
-        t : [B]
-
-        Returns
-        -------
-        Tensor
-            Predicted actions, [batch_size, num_actions, dim_action]
-        """
-        batch_size, num_actions = action_with_noise.shape[:2]
-
-        # Encode (action + proprio + time) → tokens
-        time_emb = timestep_embedding(t, self.dim_time)  # [batch_size, dim_time]
-        time_tokens = time_emb.unsqueeze(1).expand(batch_size, num_actions, self.dim_time)
-        proprio_tokens = proprio.unsqueeze(1).expand(batch_size, num_actions, proprio.shape[-1])
-        action_tokens = torch.cat([action_with_noise, proprio_tokens, time_tokens], dim=-1)
-        x = self.action_encoder(action_tokens, domain_id)  # [batch_size, num_actions, hidden_size]
-
-        # Project visual streams and concatenate
-        if self.use_hetero_proj:
-            x = torch.cat(
-                [
-                    x,
-                    self.vlm_proj(vlm_features, domain_id),
-                    self.aux_visual_proj(aux_visual_inputs, domain_id),
-                ],
-                dim=1,
-            )
-        else:
-            x = torch.cat([x, self.vlm_proj(vlm_features), self.aux_visual_proj(aux_visual_inputs)], dim=1)
-
-        # Add positional embeddings (truncate if needed)
-        seq_len = x.shape[1]
-        if seq_len > self.pos_emb.shape[1]:
-            raise ValueError(f"Sequence length {seq_len} exceeds max_len_seq={self.pos_emb.shape[1]}.")
-        x = x + self.pos_emb[:, :seq_len, :]
-
-        # Append soft prompts
-        if self.len_soft_prompts > 0:
-            soft_prompts = self.soft_prompt_hub(domain_id).view(
-                batch_size, self.len_soft_prompts, self.hidden_size
-            )
-            x = torch.cat([x, soft_prompts], dim=1)
-
-        # Transformer backbone
-        for block in self.blocks:
-            x = block(x)
-
-        # Decode only the action segment
-        return self.action_decoder(self.norm(x[:, :num_actions]), domain_id)

src/lerobot/policies/xvla/utils.py

@@ -1,138 +0,0 @@
-import math
-
-import numpy as np
-
-
-def mat2quat(rmat):
-    """
-    Converts given rotation matrix to quaternion.
-
-    Args:
-        rmat (np.array): 3x3 rotation matrix
-
-    Returns:
-        np.array: (x,y,z,w) float quaternion angles
-    """
-    mat = np.asarray(rmat).astype(np.float32)[:3, :3]
-
-    m00 = mat[0, 0]
-    m01 = mat[0, 1]
-    m02 = mat[0, 2]
-    m10 = mat[1, 0]
-    m11 = mat[1, 1]
-    m12 = mat[1, 2]
-    m20 = mat[2, 0]
-    m21 = mat[2, 1]
-    m22 = mat[2, 2]
-    # symmetric matrix k
-    k = np.array(
-        [
-            [m00 - m11 - m22, np.float32(0.0), np.float32(0.0), np.float32(0.0)],
-            [m01 + m10, m11 - m00 - m22, np.float32(0.0), np.float32(0.0)],
-            [m02 + m20, m12 + m21, m22 - m00 - m11, np.float32(0.0)],
-            [m21 - m12, m02 - m20, m10 - m01, m00 + m11 + m22],
-        ]
-    )
-    k /= 3.0
-    # quaternion is Eigen vector of k that corresponds to largest eigenvalue
-    w, v = np.linalg.eigh(k)
-    inds = np.array([3, 0, 1, 2])
-    q1 = v[inds, np.argmax(w)]
-    if q1[0] < 0.0:
-        np.negative(q1, q1)
-    inds = np.array([1, 2, 3, 0])
-    return q1[inds]
-
-
-def quat2axisangle(quat):
-    """
-    Converts quaternion to axis-angle format.
-    Returns a unit vector direction scaled by its angle in radians.
-
-    Args:
-        quat (np.array): (x,y,z,w) vec4 float angles
-
-    Returns:
-        np.array: (ax,ay,az) axis-angle exponential coordinates
-    """
-    # clip quaternion
-    if quat[3] > 1.0:
-        quat[3] = 1.0
-    elif quat[3] < -1.0:
-        quat[3] = -1.0
-
-    den = np.sqrt(1.0 - quat[3] * quat[3])
-    if math.isclose(den, 0.0):
-        # This is (close to) a zero degree rotation, immediately return
-        return np.zeros(3)
-
-    return (quat[:3] * 2.0 * math.acos(quat[3])) / den
-
-
-def rotate6d_to_axis_angle(r6d):
-    """
-    r6d: np.ndarray, shape (N, 6)
-    return: np.ndarray, shape (N, 3), axis-angle vectors
-    """
-    flag = 0
-    if len(r6d.shape) == 1:
-        r6d = r6d[None, ...]
-        flag = 1
-
-    a1 = r6d[:, 0:3]
-    a2 = r6d[:, 3:6]
-
-    # b1
-    b1 = a1 / (np.linalg.norm(a1, axis=-1, keepdims=True) + 1e-6)
-
-    # b2
-    dot_prod = np.sum(b1 * a2, axis=-1, keepdims=True)
-    b2_orth = a2 - dot_prod * b1
-    b2 = b2_orth / (np.linalg.norm(b2_orth, axis=-1, keepdims=True) + 1e-6)
-
-    # b3
-    b3 = np.cross(b1, b2, axis=-1)
-
-    rotation_matrix = np.stack([b1, b2, b3], axis=-1)  # shape: (N, 3, 3)
-
-    axis_angle_list = []
-    for i in range(rotation_matrix.shape[0]):
-        quat = mat2quat(rotation_matrix[i])
-        axis_angle = quat2axisangle(quat)
-        axis_angle_list.append(axis_angle)
-
-    axis_angle_array = np.stack(axis_angle_list, axis=0)  # shape: (N, 3)
-
-    if flag == 1:
-        axis_angle_array = axis_angle_array[0]
-
-    return axis_angle_array
-
-
-def mat_to_rotate6d(abs_action):
-    if len(abs_action.shape) == 2:
-        return np.concatenate([abs_action[:3, 0], abs_action[:3, 1]], axis=-1)
-    elif len(abs_action.shape) == 3:
-        return np.concatenate([abs_action[:, :3, 0], abs_action[:, :3, 1]], axis=-1)
-    else:
-        raise NotImplementedError
-
-
-def drop_path(x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-
-    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
-    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
-    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
-    'survival rate' as the argument.
-
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
-    if keep_prob > 0.0 and scale_by_keep:
-        random_tensor.div_(keep_prob)
-    return x * random_tensor

src/lerobot/processor/converters.py

@@ -170,8 +170,9 @@ def _extract_complementary_data(batch: dict[str, Any]) -> dict[str, Any]:
     task_key = {"task": batch["task"]} if "task" in batch else {}
     index_key = {"index": batch["index"]} if "index" in batch else {}
     task_index_key = {"task_index": batch["task_index"]} if "task_index" in batch else {}
+    episode_index_key = {"episode_index": batch["episode_index"]} if "episode_index" in batch else {}
 
-    return {**pad_keys, **task_key, **index_key, **task_index_key}
+    return {**pad_keys, **task_key, **index_key, **task_index_key, **episode_index_key}
 
 
 def create_transition(

src/lerobot/rl/actor.py

@@ -78,7 +78,7 @@ from lerobot.transport.utils import (
     transitions_to_bytes,
 )
 from lerobot.utils.random_utils import set_seed
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.transition import (
     Transition,
     move_state_dict_to_device,
@@ -398,7 +398,7 @@ def act_with_policy(
 
         if cfg.env.fps is not None:
             dt_time = time.perf_counter() - start_time
-            precise_sleep(1 / cfg.env.fps - dt_time)
+            busy_wait(1 / cfg.env.fps - dt_time)
 
 
 #  Communication Functions - Group all gRPC/messaging functions

src/lerobot/rl/buffer.py

@@ -15,8 +15,7 @@
 # limitations under the License.
 
 import functools
-import itertools
-from collections.abc import Callable, Generator, Sequence
+from collections.abc import Callable, Sequence
 from contextlib import suppress
 from typing import TypedDict
 
@@ -30,20 +29,13 @@ from lerobot.utils.transition import Transition
 
 
 class BatchTransition(TypedDict):
-    """Batch transition for single-step RL algorithms.
-
-    Uses Gymnasium terminology:
-    - terminated: True termination due to task success/failure
-    - truncated: Termination due to time limit or other external factors
-    """
-
     state: dict[str, torch.Tensor]
     action: torch.Tensor
     reward: torch.Tensor
     next_state: dict[str, torch.Tensor]
-    terminated: torch.Tensor  # True termination due to task success/failure
-    truncated: torch.Tensor  # Termination due to time limit
-    complementary_info: dict[str, torch.Tensor] | None
+    done: torch.Tensor
+    truncated: torch.Tensor
+    complementary_info: dict[str, torch.Tensor | float | int] | None = None
 
 
 def random_crop_vectorized(images: torch.Tensor, output_size: tuple) -> torch.Tensor:
@@ -86,8 +78,6 @@ def random_shift(images: torch.Tensor, pad: int = 4):
 
 
 class ReplayBuffer:
-    """Replay buffer for storing transitions used in RL training (e.g., SAC)."""
-
     def __init__(
         self,
         capacity: int,
@@ -143,7 +133,7 @@ class ReplayBuffer:
         self,
         state: dict[str, torch.Tensor],
         action: torch.Tensor,
-        complementary_info: dict[str, torch.Tensor | float | int] | None = None,
+        complementary_info: dict[str, torch.Tensor] | None = None,
     ):
         """Initialize the storage tensors based on the first transition."""
         # Determine shapes from the first transition
@@ -169,8 +159,8 @@ class ReplayBuffer:
             # Just create a reference to states for consistent API
             self.next_states = self.states  # Just a reference for API consistency
 
-        self.terminated = torch.empty((self.capacity,), dtype=torch.bool, device=self.storage_device)
-        self.truncated = torch.empty((self.capacity,), dtype=torch.bool, device=self.storage_device)
+        self.dones = torch.empty((self.capacity,), dtype=torch.bool, device=self.storage_device)
+        self.truncateds = torch.empty((self.capacity,), dtype=torch.bool, device=self.storage_device)
 
         # Initialize storage for complementary_info
         self.has_complementary_info = complementary_info is not None
@@ -205,7 +195,7 @@ class ReplayBuffer:
         next_state: dict[str, torch.Tensor],
         done: bool,
         truncated: bool,
-        complementary_info: dict[str, torch.Tensor | float | int] | None = None,
+        complementary_info: dict[str, torch.Tensor] | None = None,
     ):
         """Saves a transition, ensuring tensors are stored on the designated storage device."""
         # Initialize storage if this is the first transition
@@ -222,8 +212,8 @@ class ReplayBuffer:
 
         self.actions[self.position].copy_(action.squeeze(dim=0))
         self.rewards[self.position] = reward
-        self.terminated[self.position] = done
-        self.truncated[self.position] = truncated
+        self.dones[self.position] = done
+        self.truncateds[self.position] = truncated
 
         # Handle complementary_info if provided and storage is initialized
         if complementary_info is not None and self.has_complementary_info:
@@ -293,8 +283,8 @@ class ReplayBuffer:
         # Sample other tensors
         batch_actions = self.actions[idx].to(self.device)
         batch_rewards = self.rewards[idx].to(self.device)
-        batch_terminated = self.terminated[idx].to(self.device).float()
-        batch_truncated = self.truncated[idx].to(self.device).float()
+        batch_dones = self.dones[idx].to(self.device).float()
+        batch_truncateds = self.truncateds[idx].to(self.device).float()
 
         # Sample complementary_info if available
         batch_complementary_info = None
@@ -308,8 +298,8 @@ class ReplayBuffer:
             action=batch_actions,
             reward=batch_rewards,
             next_state=batch_next_state,
-            terminated=batch_terminated,
-            truncated=batch_truncated,
+            done=batch_dones,
+            truncated=batch_truncateds,
             complementary_info=batch_complementary_info,
         )
 
@@ -441,6 +431,7 @@ class ReplayBuffer:
             device (str): The device for sampling tensors. Defaults to "cuda:0".
             state_keys (Sequence[str] | None): The list of keys that appear in `state` and `next_state`.
             capacity (int | None): Buffer capacity. If None, uses dataset length.
+            action_mask (Sequence[int] | None): Indices of action dimensions to keep.
             image_augmentation_function (Callable | None): Function for image augmentation.
                 If None, uses default random shift with pad=4.
             use_drq (bool): Whether to use DrQ image augmentation when sampling.
@@ -469,16 +460,12 @@ class ReplayBuffer:
             optimize_memory=optimize_memory,
         )
 
-        # Convert dataset to transitions generator
-        transitions_generator = cls._lerobotdataset_to_transitions(
-            dataset=lerobot_dataset, state_keys=state_keys
-        )
-
-        # Get first transition to initialize storage
-        first_transition = next(transitions_generator, None)
+        # Convert dataset to transitions
+        list_transition = cls._lerobotdataset_to_transitions(dataset=lerobot_dataset, state_keys=state_keys)
 
         # Initialize the buffer with the first transition to set up storage tensors
-        if first_transition is not None:
+        if list_transition:
+            first_transition = list_transition[0]
             first_state = {k: v.to(device) for k, v in first_transition["state"].items()}
             first_action = first_transition[ACTION].to(device)
 
@@ -496,28 +483,26 @@ class ReplayBuffer:
                 state=first_state, action=first_action, complementary_info=first_complementary_info
             )
 
-        # Fill the buffer with all transitions (first + remaining)
-        if first_transition is not None:
-            for data in itertools.chain([first_transition], transitions_generator):
-                for k, v in data.items():
-                    if isinstance(v, dict):
-                        for key, tensor in v.items():
-                            if isinstance(tensor, torch.Tensor):
-                                v[key] = tensor.to(storage_device)
-                    elif isinstance(v, torch.Tensor):
-                        data[k] = v.to(storage_device)
+        # Fill the buffer with all transitions
+        for data in list_transition:
+            for k, v in data.items():
+                if isinstance(v, dict):
+                    for key, tensor in v.items():
+                        v[key] = tensor.to(storage_device)
+                elif isinstance(v, torch.Tensor):
+                    data[k] = v.to(storage_device)
 
-                action = data[ACTION]
+            action = data[ACTION]
 
-                replay_buffer.add(
-                    state=data["state"],
-                    action=action,
-                    reward=data["reward"],
-                    next_state=data["next_state"],
-                    done=data["done"],
-                    truncated=data["truncated"],
-                    complementary_info=data.get("complementary_info"),
-                )
+            replay_buffer.add(
+                state=data["state"],
+                action=action,
+                reward=data["reward"],
+                next_state=data["next_state"],
+                done=data["done"],
+                truncated=False,  # NOTE: Truncation are not supported yet in lerobot dataset
+                complementary_info=data.get("complementary_info", None),
+            )
 
         return replay_buffer
 
@@ -591,12 +576,10 @@ class ReplayBuffer:
             for key in self.states:
                 frame_dict[key] = self.states[key][actual_idx].cpu()
 
-            # Fill action, reward, done (done = terminated or truncated)
+            # Fill action, reward, done
             frame_dict[ACTION] = self.actions[actual_idx].cpu()
             frame_dict[REWARD] = torch.tensor([self.rewards[actual_idx]], dtype=torch.float32).cpu()
-            frame_dict[DONE] = torch.tensor(
-                [self.terminated[actual_idx] or self.truncated[actual_idx]], dtype=torch.bool
-            ).cpu()
+            frame_dict[DONE] = torch.tensor([self.dones[actual_idx]], dtype=torch.bool).cpu()
             frame_dict["task"] = task_name
 
             # Add complementary_info if available
@@ -616,7 +599,7 @@ class ReplayBuffer:
             lerobot_dataset.add_frame(frame_dict)
 
             # If we reached an episode boundary, call save_episode, reset counters
-            if self.terminated[actual_idx] or self.truncated[actual_idx]:
+            if self.dones[actual_idx] or self.truncateds[actual_idx]:
                 lerobot_dataset.save_episode()
 
         # Save any remaining frames in the buffer
@@ -632,11 +615,9 @@ class ReplayBuffer:
     def _lerobotdataset_to_transitions(
         dataset: LeRobotDataset,
         state_keys: Sequence[str] | None = None,
-    ) -> Generator[Transition, None, None]:
+    ) -> list[Transition]:
         """
-        Convert a LeRobotDataset into a generator of RL (s, a, r, s', done) transitions.
-
-        Using a generator instead of a list is more memory efficient for large datasets.
+        Convert a LeRobotDataset into a list of RL (s, a, r, s', done) transitions.
 
         Args:
             dataset (LeRobotDataset):
@@ -656,12 +637,14 @@ class ReplayBuffer:
                 ["observation.state", "observation.environment_state"].
                 If None, you must handle or define default keys.
 
-        Yields:
-            Transition: A transition dictionary.
+        Returns:
+            transitions (List[Transition]):
+                A list of Transition dictionaries with the same length as `dataset`.
         """
         if state_keys is None:
             raise ValueError("State keys must be provided when converting LeRobotDataset to Transitions.")
 
+        transitions = []
         num_frames = len(dataset)
 
         # Check if the dataset has "next.done" key
@@ -704,17 +687,8 @@ class ReplayBuffer:
                     if next_sample["episode_index"] != current_sample["episode_index"]:
                         done = True
 
-            # Handle truncation separately from done
-            # This is important if the dataset has truncations (e.g., time limits)
-            truncated = False
-            if not done:
-                # If this is the last frame or if next frame is in a different episode, mark as truncated
-                if i == num_frames - 1:
-                    truncated = True
-                elif i < num_frames - 1:
-                    next_sample = dataset[i + 1]
-                    if next_sample["episode_index"] != current_sample["episode_index"]:
-                        truncated = True
+            # TODO: (azouitine) Handle truncation (using the same value as done for now)
+            truncated = done
 
             # ----- 4) Next state -----
             # If not done and the next sample is in the same episode, we pull the next sample's state.
@@ -742,6 +716,7 @@ class ReplayBuffer:
                     if isinstance(val, torch.Tensor):
                         complementary_info[clean_key] = val.unsqueeze(0)  # Add batch dimension
                     else:
+                        # TODO: (azouitine) Check if it's necessary to convert to tensor
                         # For non-tensor values, use directly
                         complementary_info[clean_key] = val
 
@@ -755,10 +730,12 @@ class ReplayBuffer:
                 truncated=truncated,
                 complementary_info=complementary_info,
             )
+            transitions.append(transition)
 
-            yield transition
+        return transitions
 
 
+# Utility function to guess shapes/dtypes from a tensor
 def guess_feature_info(t, name: str):
     """
     Return a dictionary with the 'dtype' and 'shape' for a given tensor or scalar value.
@@ -828,9 +805,9 @@ def concatenate_batch_transitions(
         for key in left_batch_transitions["next_state"]
     }
 
-    # Concatenate terminated and truncated fields
-    left_batch_transitions["terminated"] = torch.cat(
-        [left_batch_transitions["terminated"], right_batch_transition["terminated"]], dim=0
+    # Concatenate done and truncated fields
+    left_batch_transitions["done"] = torch.cat(
+        [left_batch_transitions["done"], right_batch_transition["done"]], dim=0
     )
     left_batch_transitions["truncated"] = torch.cat(
         [left_batch_transitions["truncated"], right_batch_transition["truncated"]],

src/lerobot/rl/gym_manipulator.py

@@ -74,7 +74,7 @@ from lerobot.teleoperators import (
 from lerobot.teleoperators.teleoperator import Teleoperator
 from lerobot.teleoperators.utils import TeleopEvents
 from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, REWARD
-from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.utils import log_say
 
 logging.basicConfig(level=logging.INFO)
@@ -114,7 +114,7 @@ def reset_follower_position(robot_arm: Robot, target_position: np.ndarray) -> No
     for pose in trajectory:
         action_dict = dict(zip(current_position_dict, pose, strict=False))
         robot_arm.bus.sync_write("Goal_Position", action_dict)
-        precise_sleep(0.015)
+        busy_wait(0.015)
 
 
 class RobotEnv(gym.Env):
@@ -238,7 +238,7 @@ class RobotEnv(gym.Env):
             reset_follower_position(self.robot, np.array(self.reset_pose))
             log_say("Reset the environment done.", play_sounds=True)
 
-        precise_sleep(self.reset_time_s - (time.perf_counter() - start_time))
+        busy_wait(self.reset_time_s - (time.perf_counter() - start_time))
 
         super().reset(seed=seed, options=options)
 
@@ -713,7 +713,7 @@ def control_loop(
             transition = env_processor(transition)
 
         # Maintain fps timing
-        precise_sleep(dt - (time.perf_counter() - step_start_time))
+        busy_wait(dt - (time.perf_counter() - step_start_time))
 
     if dataset is not None and cfg.dataset.push_to_hub:
         logging.info("Pushing dataset to hub")
@@ -745,7 +745,7 @@ def replay_trajectory(
         )
         transition = action_processor(transition)
         env.step(transition[TransitionKey.ACTION])
-        precise_sleep(1 / cfg.env.fps - (time.perf_counter() - start_time))
+        busy_wait(1 / cfg.env.fps - (time.perf_counter() - start_time))
 
 
 @parser.wrap()

src/lerobot/robots/earthrover_mini_plus/config_earthrover_mini_plus.py

@@ -1,35 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Configuration for EarthRover Mini Plus robot."""
-
-from dataclasses import dataclass
-
-from ..config import RobotConfig
-
-
-@RobotConfig.register_subclass("earthrover_mini_plus")
-@dataclass
-class EarthRoverMiniPlusConfig(RobotConfig):
-    """Configuration for EarthRover Mini Plus robot using Frodobots SDK.
-
-    This robot uses cloud-based control via the Frodobots SDK HTTP API.
-    Camera frames are accessed directly through SDK HTTP endpoints.
-
-    Attributes:
-        sdk_url: URL of the Frodobots SDK server (default: http://localhost:8000)
-    """
-
-    sdk_url: str = "http://localhost:8000"

src/lerobot/robots/earthrover_mini_plus/earthrover_mini_plus.mdx

@@ -1 +0,0 @@
-../../../../docs/source/earthrover_mini_plus.mdx

src/lerobot/robots/earthrover_mini_plus/robot_earthrover_mini_plus.py

@@ -1,473 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""EarthRover Mini Plus robot using Frodobots SDK."""
-
-import base64
-import logging
-from functools import cached_property
-from typing import Any
-
-import cv2
-import numpy as np
-import requests
-
-from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
-
-from ..robot import Robot
-from .config_earthrover_mini_plus import EarthRoverMiniPlusConfig
-
-logger = logging.getLogger(__name__)
-
-# Action feature keys
-ACTION_LINEAR_VEL = "linear.vel"
-ACTION_ANGULAR_VEL = "angular.vel"
-
-# Observation feature keys
-OBS_FRONT = "front"
-OBS_REAR = "rear"
-OBS_LINEAR_VEL = "linear.vel"
-OBS_BATTERY_LEVEL = "battery.level"
-OBS_ORIENTATION_DEG = "orientation.deg"
-OBS_GPS_LATITUDE = "gps.latitude"
-OBS_GPS_LONGITUDE = "gps.longitude"
-OBS_GPS_SIGNAL = "gps.signal"
-OBS_SIGNAL_LEVEL = "signal.level"
-OBS_VIBRATION = "vibration"
-OBS_LAMP_STATE = "lamp.state"
-
-
-class EarthRoverMiniPlus(Robot):
-    """
-    EarthRover Mini Plus robot controlled via Frodobots SDK HTTP API.
-
-    This robot uses cloud-based control through the Frodobots SDK instead of direct
-    hardware connection. Cameras stream via WebRTC through Agora cloud, and control
-    commands are sent via HTTP POST requests.
-
-    The robot supports:
-    - Dual cameras (front and rear) accessed via SDK HTTP endpoints
-    - Linear and angular velocity control
-    - Battery and orientation telemetry
-
-    Attributes:
-        config: Robot configuration
-        sdk_base_url: URL of the Frodobots SDK server (default: http://localhost:8000)
-    """
-
-    config_class = EarthRoverMiniPlusConfig
-    name = "earthrover_mini_plus"
-
-    def __init__(self, config: EarthRoverMiniPlusConfig):
-        """Initialize EarthRover Mini Plus robot.
-
-        Args:
-            config: Robot configuration including SDK URL
-        """
-        super().__init__(config)
-        self.config = config
-        self.sdk_base_url = "http://localhost:8000"
-
-        # Empty cameras dict for compatibility with recording script
-        # Cameras are accessed directly via SDK, not through Camera objects
-        self.cameras = {}
-        self._is_connected = False
-
-        # Cache for camera frames (fallback when requests fail)
-        self._last_front_frame = None
-        self._last_rear_frame = None
-
-        # Cache for robot telemetry data (fallback when requests fail)
-        self._last_robot_data = None
-
-        logger.info(f"Initialized {self.name} with SDK at {self.sdk_base_url}")
-
-    @property
-    def is_connected(self) -> bool:
-        """Check if robot is connected to SDK."""
-        return self._is_connected
-
-    def connect(self, calibrate: bool = True) -> None:
-        """Connect to robot via Frodobots SDK.
-
-        Args:
-            calibrate: Not used for SDK-based robot (kept for API compatibility)
-
-        Raises:
-            DeviceAlreadyConnectedError: If robot is already connected
-            DeviceNotConnectedError: If cannot connect to SDK server
-        """
-        if self._is_connected:
-            raise DeviceAlreadyConnectedError(f"{self.name} is already connected")
-
-        # Verify SDK is running and accessible
-        try:
-            response = requests.get(f"{self.sdk_base_url}/data", timeout=10.0)
-            if response.status_code != 200:
-                raise DeviceNotConnectedError(
-                    f"Cannot connect to SDK at {self.sdk_base_url}. "
-                    "Make sure it's running: hypercorn main:app --reload"
-                )
-        except requests.RequestException as e:
-            raise DeviceNotConnectedError(f"Cannot connect to SDK at {self.sdk_base_url}: {e}") from e
-
-        self._is_connected = True
-        logger.info(f"{self.name} connected to SDK")
-
-        if calibrate:
-            self.calibrate()
-
-    def calibrate(self) -> None:
-        """Calibration not needed for SDK-based robot."""
-        logger.info("Calibration not required for SDK-based robot")
-
-    @property
-    def is_calibrated(self) -> bool:
-        """SDK robot doesn't require calibration.
-
-        Returns:
-            bool: Always True for SDK-based robots
-        """
-        return True
-
-    def configure(self) -> None:
-        """Configure robot (no-op for SDK-based robot)."""
-        pass
-
-    @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
-        """Define the observation space for dataset recording.
-
-        Returns:
-            dict: Observation features with types/shapes:
-                - front: (480, 640, 3) - Front camera RGB image
-                - rear: (480, 640, 3) - Rear camera RGB image
-                - linear.vel: float - Current speed (0-1, SDK reports only positive speeds)
-                - battery.level: float - Battery level (0-1, normalized from 0-100)
-                - orientation.deg: float - Robot orientation (0-1, normalized from raw value)
-                - gps.latitude: float - GPS latitude coordinate
-                - gps.longitude: float - GPS longitude coordinate
-                - gps.signal: float - GPS signal strength (0-1, normalized from percentage)
-                - signal.level: float - Network signal level (0-1, normalized from 0-5)
-                - vibration: float - Vibration sensor reading
-                - lamp.state: float - Lamp state (0=off, 1=on)
-        """
-        return {
-            # Cameras (height, width, channels)
-            OBS_FRONT: (480, 640, 3),
-            OBS_REAR: (480, 640, 3),
-            # Motion state
-            OBS_LINEAR_VEL: float,
-            # Robot state
-            OBS_BATTERY_LEVEL: float,
-            OBS_ORIENTATION_DEG: float,
-            # GPS
-            OBS_GPS_LATITUDE: float,
-            OBS_GPS_LONGITUDE: float,
-            OBS_GPS_SIGNAL: float,
-            # Sensors
-            OBS_SIGNAL_LEVEL: float,
-            OBS_VIBRATION: float,
-            OBS_LAMP_STATE: float,
-        }
-
-    @cached_property
-    def action_features(self) -> dict[str, type]:
-        """Define the action space.
-
-        Returns:
-            dict: Action features with types:
-                - linear.vel: float - Target linear velocity
-                - angular.vel: float - Target angular velocity
-        """
-        return {
-            ACTION_LINEAR_VEL: float,
-            ACTION_ANGULAR_VEL: float,
-        }
-
-    def get_observation(self) -> dict[str, Any]:
-        """Get current robot observation from SDK.
-
-        Returns:
-            dict: Observation containing:
-                - front: Front camera image (480, 640, 3) in RGB format
-                - rear: Rear camera image (480, 640, 3) in RGB format
-                - linear.vel: Current speed (0-1, SDK reports only positive speeds)
-                - battery.level: Battery level (0-1, normalized from 0-100)
-                - orientation.deg: Robot orientation (0-1, normalized from raw value)
-                - gps.latitude: GPS latitude coordinate
-                - gps.longitude: GPS longitude coordinate
-                - gps.signal: GPS signal strength (0-1, normalized from percentage)
-                - signal.level: Network signal level (0-1, normalized from 0-5)
-                - vibration: Vibration sensor reading
-                - lamp.state: Lamp state (0=off, 1=on)
-
-        Raises:
-            DeviceNotConnectedError: If robot is not connected
-
-        Note:
-            Camera frames are retrieved from SDK endpoints /v2/front and /v2/rear.
-            Frames are decoded from base64 and converted from BGR to RGB format.
-            Robot telemetry is retrieved from /data endpoint.
-            All SDK values are normalized to appropriate ranges for dataset recording.
-        """
-        if not self._is_connected:
-            raise DeviceNotConnectedError(f"{self.name} is not connected")
-
-        observation = {}
-
-        # Get camera images from SDK
-        frames = self._get_camera_frames()
-        observation[OBS_FRONT] = frames["front"]
-        observation[OBS_REAR] = frames["rear"]
-
-        # Get robot state from SDK
-        robot_data = self._get_robot_data()
-
-        # Motion state
-        observation[OBS_LINEAR_VEL] = robot_data["speed"] / 100.0  # Normalize 0-100 to 0-1
-
-        # Robot state
-        observation[OBS_BATTERY_LEVEL] = robot_data["battery"] / 100.0  # Normalize 0-100 to 0-1
-        observation[OBS_ORIENTATION_DEG] = robot_data["orientation"] / 360.0  # Normalize to 0-1
-
-        # GPS data
-        observation[OBS_GPS_LATITUDE] = robot_data["latitude"]
-        observation[OBS_GPS_LONGITUDE] = robot_data["longitude"]
-        observation[OBS_GPS_SIGNAL] = robot_data["gps_signal"] / 100.0  # Normalize percentage to 0-1
-
-        # Sensors
-        observation[OBS_SIGNAL_LEVEL] = robot_data["signal_level"] / 5.0  # Normalize 0-5 to 0-1
-        observation[OBS_VIBRATION] = robot_data["vibration"]
-        observation[OBS_LAMP_STATE] = float(robot_data["lamp"])  # 0 or 1
-
-        return observation
-
-    def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
-        """Send action to robot via SDK.
-
-        Args:
-            action: Action dict with keys:
-                - linear.vel: Target linear velocity (-1 to 1)
-                - angular.vel: Target angular velocity (-1 to 1)
-
-        Returns:
-            dict: The action that was sent (matches action_features keys)
-
-        Raises:
-            DeviceNotConnectedError: If robot is not connected
-
-        Note:
-            Actions are sent to SDK via POST /control endpoint.
-            SDK expects commands in range [-1, 1].
-        """
-        if not self._is_connected:
-            raise DeviceNotConnectedError(f"{self.name} is not connected")
-
-        # Extract action values and convert to float
-        linear = float(action.get(ACTION_LINEAR_VEL, 0.0))
-        angular = float(action.get(ACTION_ANGULAR_VEL, 0.0))
-
-        # Send command to SDK
-        try:
-            self._send_command_to_sdk(linear, angular)
-        except Exception as e:
-            logger.error(f"Error sending action: {e}")
-
-        # Return action in format matching action_features
-        return {
-            ACTION_LINEAR_VEL: linear,
-            ACTION_ANGULAR_VEL: angular,
-        }
-
-    def disconnect(self) -> None:
-        """Disconnect from robot.
-
-        Stops the robot and closes connection to SDK.
-
-        Raises:
-            DeviceNotConnectedError: If robot is not connected
-        """
-        if not self._is_connected:
-            raise DeviceNotConnectedError(f"{self.name} is not connected")
-
-        # Stop the robot before disconnecting
-        try:
-            self._send_command_to_sdk(0.0, 0.0)
-        except Exception as e:
-            logger.warning(f"Failed to stop robot during disconnect: {e}")
-
-        self._is_connected = False
-        logger.info(f"{self.name} disconnected")
-
-    # Private helper methods for SDK communication
-
-    def _get_camera_frames(self) -> dict[str, np.ndarray]:
-        """Get camera frames from SDK using v2 endpoints with caching fallback.
-
-        Returns:
-            dict: Dictionary with 'front' and 'rear' keys containing:
-                - Current frame (if request succeeds)
-                - Cached frame (if request fails but cache exists)
-                - Zero array (if request fails and no cache exists yet)
-
-        Note:
-            Uses /v2/front and /v2/rear endpoints which are 15x faster than /screenshot.
-            Images are base64 encoded, resized to 640x480, and converted from BGR to RGB.
-            If request fails, returns the last successfully retrieved frame (cached).
-        """
-        frames = {}
-
-        # Get front camera
-        try:
-            response = requests.get(f"{self.sdk_base_url}/v2/front", timeout=2.0)
-            if response.status_code == 200:
-                data = response.json()
-                if "front_frame" in data and data["front_frame"]:
-                    front_img = self._decode_base64_image(data["front_frame"])
-                    if front_img is not None:
-                        # Resize and convert BGR to RGB
-                        front_img = cv2.resize(front_img, (640, 480))
-                        front_rgb = cv2.cvtColor(front_img, cv2.COLOR_BGR2RGB)
-                        frames["front"] = front_rgb
-                        # Cache the successful frame
-                        self._last_front_frame = front_rgb
-        except Exception as e:
-            logger.warning(f"Error fetching front camera: {e}")
-
-        # Fallback: use cache or zero array
-        if "front" not in frames:
-            if self._last_front_frame is not None:
-                frames["front"] = self._last_front_frame
-            else:
-                frames["front"] = np.zeros((480, 640, 3), dtype=np.uint8)
-
-        # Get rear camera
-        try:
-            response = requests.get(f"{self.sdk_base_url}/v2/rear", timeout=2.0)
-            if response.status_code == 200:
-                data = response.json()
-                if "rear_frame" in data and data["rear_frame"]:
-                    rear_img = self._decode_base64_image(data["rear_frame"])
-                    if rear_img is not None:
-                        # Resize and convert BGR to RGB
-                        rear_img = cv2.resize(rear_img, (640, 480))
-                        rear_rgb = cv2.cvtColor(rear_img, cv2.COLOR_BGR2RGB)
-                        frames["rear"] = rear_rgb
-                        # Cache the successful frame
-                        self._last_rear_frame = rear_rgb
-        except Exception as e:
-            logger.warning(f"Error fetching rear camera: {e}")
-
-        # Fallback: use cache or zero array
-        if "rear" not in frames:
-            if self._last_rear_frame is not None:
-                frames["rear"] = self._last_rear_frame
-            else:
-                frames["rear"] = np.zeros((480, 640, 3), dtype=np.uint8)
-
-        return frames
-
-    def _decode_base64_image(self, base64_string: str) -> np.ndarray | None:
-        """Decode base64 string to image.
-
-        Args:
-            base64_string: Base64 encoded image string
-
-        Returns:
-            np.ndarray: Decoded image in BGR format (OpenCV default), or None if decoding fails
-        """
-        try:
-            img_bytes = base64.b64decode(base64_string)
-            nparr = np.frombuffer(img_bytes, np.uint8)
-            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-            return img  # Return in BGR format (OpenCV default)
-        except Exception as e:
-            logger.error(f"Error decoding image: {e}")
-            return None
-
-    def _get_robot_data(self) -> dict:
-        """Get robot telemetry data from SDK.
-
-        Returns:
-            dict: Robot telemetry data including battery, speed, orientation, GPS, etc:
-                - Current data (if request succeeds)
-                - Cached data (if request fails but cache exists)
-                - Default values (if request fails and no cache exists yet)
-
-        Note:
-            Uses /data endpoint which provides comprehensive robot state.
-            If request fails, returns the last successfully retrieved data (cached).
-        """
-        try:
-            response = requests.get(f"{self.sdk_base_url}/data", timeout=2.0)
-            if response.status_code == 200:
-                data = response.json()
-                # Cache the successful data
-                self._last_robot_data = data
-                return data
-        except Exception as e:
-            logger.warning(f"Error fetching robot data: {e}")
-
-        # Fallback: use cache or default values
-        if self._last_robot_data is not None:
-            return self._last_robot_data
-        else:
-            # Return dict with default values (used only on first failure before any cache exists)
-            return {
-                "speed": 0,
-                "battery": 0,
-                "orientation": 0,
-                "latitude": 0.0,
-                "longitude": 0.0,
-                "gps_signal": 0,
-                "signal_level": 0,
-                "vibration": 0.0,
-                "lamp": 0,
-            }
-
-    def _send_command_to_sdk(self, linear: float, angular: float, lamp: int = 0) -> bool:
-        """Send control command to SDK.
-
-        Args:
-            linear: Linear velocity command (-1 to 1)
-            angular: Angular velocity command (-1 to 1)
-            lamp: Lamp control (0=off, 1=on)
-
-        Returns:
-            bool: True if command sent successfully, False otherwise
-
-        Note:
-            Uses POST /control endpoint. Commands are sent as JSON payload.
-        """
-        try:
-            payload = {
-                "command": {
-                    "linear": linear,
-                    "angular": angular,
-                    "lamp": lamp,
-                }
-            }
-
-            response = requests.post(
-                f"{self.sdk_base_url}/control",
-                json=payload,
-                timeout=1.0,
-            )
-
-            return response.status_code == 200
-        except Exception as e:
-            logger.error(f"Error sending command: {e}")
-            return False

src/lerobot/robots/unitree_g1/__init__.py

@@ -1,18 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .config_unitree_g1 import UnitreeG1Config
-from .unitree_g1 import UnitreeG1

src/lerobot/robots/unitree_g1/config_unitree_g1.py

@@ -1,55 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass, field
-
-from ..config import RobotConfig
-
-_GAINS: dict[str, dict[str, list[float]]] = {
-    "left_leg": {
-        "kp": [150, 150, 150, 300, 40, 40],
-        "kd": [2, 2, 2, 4, 2, 2],
-    },  # pitch, roll, yaw, knee, ankle_pitch, ankle_roll
-    "right_leg": {"kp": [150, 150, 150, 300, 40, 40], "kd": [2, 2, 2, 4, 2, 2]},
-    "waist": {"kp": [250, 250, 250], "kd": [5, 5, 5]},  # yaw, roll, pitch
-    "left_arm": {"kp": [80, 80, 80, 80], "kd": [3, 3, 3, 3]},  # shoulder_pitch/roll/yaw, elbow
-    "left_wrist": {"kp": [40, 40, 40], "kd": [1.5, 1.5, 1.5]},  # roll, pitch, yaw
-    "right_arm": {"kp": [80, 80, 80, 80], "kd": [3, 3, 3, 3]},
-    "right_wrist": {"kp": [40, 40, 40], "kd": [1.5, 1.5, 1.5]},
-    "other": {"kp": [80, 80, 80, 80, 80, 80], "kd": [3, 3, 3, 3, 3, 3]},
-}
-
-
-def _build_gains() -> tuple[list[float], list[float]]:
-    """Build kp and kd lists from body-part groupings."""
-    kp = [v for g in _GAINS.values() for v in g["kp"]]
-    kd = [v for g in _GAINS.values() for v in g["kd"]]
-    return kp, kd
-
-
-_DEFAULT_KP, _DEFAULT_KD = _build_gains()
-
-
-@RobotConfig.register_subclass("unitree_g1")
-@dataclass
-class UnitreeG1Config(RobotConfig):
-    kp: list[float] = field(default_factory=lambda: _DEFAULT_KP.copy())
-    kd: list[float] = field(default_factory=lambda: _DEFAULT_KD.copy())
-
-    control_dt: float = 1.0 / 250.0  # 250Hz
-
-    # socket config for ZMQ bridge
-    robot_ip: str = "192.168.123.164"

src/lerobot/robots/unitree_g1/g1_utils.py

@@ -1,89 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from enum import IntEnum
-
-# ruff: noqa: N801, N815
-
-NUM_MOTORS = 35
-
-
-class G1_29_JointArmIndex(IntEnum):
-    # Left arm
-    kLeftShoulderPitch = 15
-    kLeftShoulderRoll = 16
-    kLeftShoulderYaw = 17
-    kLeftElbow = 18
-    kLeftWristRoll = 19
-    kLeftWristPitch = 20
-    kLeftWristyaw = 21
-
-    # Right arm
-    kRightShoulderPitch = 22
-    kRightShoulderRoll = 23
-    kRightShoulderYaw = 24
-    kRightElbow = 25
-    kRightWristRoll = 26
-    kRightWristPitch = 27
-    kRightWristYaw = 28
-
-
-class G1_29_JointIndex(IntEnum):
-    # Left leg
-    kLeftHipPitch = 0
-    kLeftHipRoll = 1
-    kLeftHipYaw = 2
-    kLeftKnee = 3
-    kLeftAnklePitch = 4
-    kLeftAnkleRoll = 5
-
-    # Right leg
-    kRightHipPitch = 6
-    kRightHipRoll = 7
-    kRightHipYaw = 8
-    kRightKnee = 9
-    kRightAnklePitch = 10
-    kRightAnkleRoll = 11
-
-    kWaistYaw = 12
-    kWaistRoll = 13
-    kWaistPitch = 14
-
-    # Left arm
-    kLeftShoulderPitch = 15
-    kLeftShoulderRoll = 16
-    kLeftShoulderYaw = 17
-    kLeftElbow = 18
-    kLeftWristRoll = 19
-    kLeftWristPitch = 20
-    kLeftWristyaw = 21
-
-    # Right arm
-    kRightShoulderPitch = 22
-    kRightShoulderRoll = 23
-    kRightShoulderYaw = 24
-    kRightElbow = 25
-    kRightWristRoll = 26
-    kRightWristPitch = 27
-    kRightWristYaw = 28
-
-    # not used
-    kNotUsedJoint0 = 29
-    kNotUsedJoint1 = 30
-    kNotUsedJoint2 = 31
-    kNotUsedJoint3 = 32
-    kNotUsedJoint4 = 33
-    kNotUsedJoint5 = 34