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/fast_tests.yml

@@ -60,19 +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/unbound_deps_tests.yml

@@ -45,19 +45,12 @@ 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:
           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/xvla.mdx

@@ -1,570 +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: 32%; max-width: 450px; 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 the following freezing strategy:
-
-```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" \
-  --steps=3000 \
-  --policy.device=cuda \
-  --policy.freeze_vision_encoder=True \
-  --policy.freeze_language_encoder=True \
-  --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`    | `True`  | Freeze the VLM vision encoder weights    |
-| `freeze_language_encoder`  | `True`  | 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, freeze the VLM encoders and only train the policy transformer and soft prompts. This provides excellent sample efficiency with minimal compute.
-
-### 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.repo_id="YOUR_USERNAME/xvla-biso101" \
-  --steps=3000 \
-  --policy.device=cuda \
-  --policy.action_mode=so101_bimanual \
-  --policy.freeze_vision_encoder=True \
-  --policy.freeze_language_encoder=True \
-  --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.
-To enable this behavior, you must:
-
-1. Implement a custom optimizer and register it in your training config
-
-```
-from dataclasses import dataclass, asdict
-from lerobot.optim.optimizers import OptimizerConfig
-import torch
-
-@OptimizerConfig.register_subclass("xvla-adamw")
-@dataclass
-class XVLAAdamW(OptimizerConfig):
-    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
-
-    def build(self, params: dict) -> torch.optim.Optimizer:
-        """
-        Expect `named_parameters()` as input.
-        Apply lr = lr / 10 for all VLM-related parameters.
-        """
-        assert isinstance(params, dict), \
-            "Custom LR optimizer requires `named_parameters()` as inputs."
-        kwargs = asdict(self)
-        kwargs.pop("grad_clip_norm")
-        vlm_group, other_group = [], []
-        for name, p in params.items():
-            if not p.requires_grad:
-                continue
-            if "vlm" in name.lower():
-                vlm_group.append(p)
-            else:
-                other_group.append(p)
-
-        param_groups = [
-            {"params": vlm_group, "lr": self.lr * 0.1, "weight_decay": self.weight_decay * 0.1},
-            {"params": other_group, "lr": self.lr, "weight_decay": self.weight_decay},
-        ]
-
-        return torch.optim.AdamW(param_groups, **kwargs)
-```
-
-2. Modify X-VLA’s get_optim_params to return named parameters
-
-Replace:
-
-```
-def get_optim_params(self) -> dict:
-    """Return only trainable parameters for optimization."""
-    return filter(lambda p: p.requires_grad, self.parameters())
-```
-
-with:
-
-```
-def get_optim_params(self):
-    """Return trainable named parameters."""
-    return filter(lambda kv: kv[1].requires_grad, self.named_parameters())
-```
-
-This ensures the optimizer receives a dict of named parameters, allowing it to correctly detect VLM modules and apply the 1/10 LR rule.
-
-❕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.
-
-### 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/PGEN_SUMMARY.md

@@ -1,243 +0,0 @@
-# Synthetic Data Generation Script - Summary
-
-## ✅ What Was Created
-
-### Main Script: `annotate_pgen.py` (717 lines)
-A production-ready script implementing the Hi-Robot synthetic data generation pipeline.
-
-**Key Features:**
-- ✅ Loads LeRobot datasets with skill annotations
-- ✅ Generates synthetic user prompts and robot utterances using Qwen VLM
-- ✅ **Temporal sampling** - generates dialogue every N seconds (default: 1s)
-- ✅ Adds `task_index_high_level` feature to dataset parquets
-- ✅ Saves high-level tasks to `meta/tasks_high_level.parquet`
-- ✅ Exports debug JSONL for quality analysis
-- ✅ Supports both Qwen2-VL and Qwen3-VL models
-- ✅ Multi-view camera support
-- ✅ Episode-aware processing with automatic first-frame sampling
-- ✅ Modular architecture for easy extension
-
-### Supporting Files Created
-
-1. **`run_pgen.sh`** - Convenience script with sensible defaults
-2. **`README_PGEN.md`** - Comprehensive documentation with examples
-3. **`example_pgen_usage.md`** - Practical examples and performance estimates
-4. **`SAMPLING_DIAGRAM.md`** - Visual explanation of temporal sampling strategy
-5. **`PGEN_SUMMARY.md`** - This file
-
-## 🚀 Key Innovation: Temporal Sampling
-
-The script processes **ALL episodes** in the dataset efficiently via `--sample-interval`:
-
-```bash
-# Instead of calling VLM for every frame (expensive):
-# 15,000 frames × VLM call = ~5 hours
-
-# Generate dialogue every 1 second (efficient):
-python annotate_pgen.py --repo-id dataset --model qwen --sample-interval 1.0
-# 15,000 frames processed, only ~500 VLM calls (30x speedup!)
-```
-
-**How it works:**
-- Process ALL frames in ALL episodes (complete coverage)
-- Generate dialogue at sampled timepoints (e.g., every 1 second)
-- Propagate task indices to intermediate frames
-- Always sample first frame of each episode
-- All frames get labeled, but VLM is only called for samples
-- No dummy values or skipped episodes
-
-**Benefits:**
-- 30-100x speedup depending on interval
-- Maintains temporal coherence
-- Reduces cost without losing quality
-- Configurable based on skill duration
-
-## 📊 Efficiency Comparison
-
-For a typical 15,000 frame dataset at 30 fps:
-
-| Method | VLM Calls | Time | Cost |
-|--------|-----------|------|------|
-| Every frame | 15,000 | ~5 hours | $$$$ |
-| Every 0.5s | 1,000 | ~20 min | $$$ |
-| **Every 1s** (default) | **500** | **~10 min** | **$$** |
-| Every 2s | 250 | ~5 min | $ |
-
-## 🎯 Usage
-
-### Quick Test (5s sampling for fast iteration)
-```bash
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --sample-interval 5.0 \
-    --output-dir ./outputs/test_quick
-```
-
-### Production Run (Recommended Settings)
-```bash
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --sample-interval 1.0 \
-    --output-dir ./outputs/full_pgen
-```
-
-### High-Quality with Qwen3
-```bash
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen3-VL-30B-A3B-Instruct \
-    --sample-interval 0.5 \
-    --temperature 0.6 \
-    --output-dir ./outputs/high_quality
-```
-
-## 📦 Output Structure
-
-After running, you'll have:
-
-```
-dataset_root/
-├── meta/
-│   ├── tasks_high_level.parquet      # High-level tasks with prompts/utterances
-│   └── syn_annotations.jsonl         # Debug: full context for each sample
-└── data/
-    └── chunk-000/
-        └── file-000.parquet           # Updated with task_index_high_level
-```
-
-**New feature added to all parquet files:**
-- `task_index_high_level` (int64): Links to tasks_high_level.parquet
-
-## 🔧 All Parameters
-
-| Parameter | Default | Description |
-|-----------|---------|-------------|
-| `--repo-id` / `--data-dir` | - | Dataset source |
-| `--model` | Qwen/Qwen2-VL-7B-Instruct | VLM model |
-| `--device` | cuda | Device to use |
-| `--dtype` | bfloat16 | Model precision |
-| `--temperature` | 0.7 | Sampling temperature |
-| **`--sample-interval`** | **1.0** | **Generate every N seconds (all episodes processed)** |
-| `--num-image-views-per-sample` | 1 | Number of cameras |
-| `--batch-size` | 1 | Batch size (currently unused) |
-| `--output-dir` | None | Output directory |
-| `--push-to-hub` | False | Push to HuggingFace |
-
-## 🎨 Generated Data Format
-
-Each sampled frame produces:
-
-```json
-{
-  "scenario_type": "specific_object",
-  "response_type": "confirmation",
-  "user_prompt": "Can you pick up the pink brick?",
-  "robot_utterance": "Sure, I'll grab the pink lego brick.",
-  "skill": "robot arm picks up pink lego brick",
-  "episode_id": 0,
-  "frame_index": 45,
-  "timestamp": 1.5,
-  "skill_history": ["robot arm moves towards pink lego brick"],
-  "task_description": "pink lego brick into the transparent box"
-}
-```
-
-**Scenario Types:**
-- specific_object, negative_task, situated_correction, implicit_request, constraint_based
-
-**Response Types:**
-- confirmation, clarification, acknowledgment, constraint_acknowledgment
-
-## 🔬 Code Architecture
-
-```python
-# Main components (modular design)
-
-class QwenPgen:
-    """VLM wrapper supporting Qwen2/3"""
-    def call_qwen(images, prompt) -> dict
-
-def construct_prompt(task, history, skill) -> str:
-    """Build contextual prompt with history"""
-
-def annotate_sample(pgen, images, ...) -> dict:
-    """Generate dialogue for one sample"""
-
-def generate_synthetic_data(dataset, pgen, ...) -> tuple:
-    """Process entire dataset with temporal sampling"""
-    # Core sampling logic:
-    # - Track last_sample_timestamp per episode
-    # - Sample if time_elapsed >= sample_interval
-    # - Always sample first frame of episodes
-    # - Propagate task_index to intermediate frames
-
-def main():
-    """CLI entrypoint with argparse"""
-```
-
-## ✨ Next Steps
-
-1. **Quick test with large interval:**
-   ```bash
-   # Fast iteration - samples every 5 seconds
-   python examples/dataset/annotate_pgen.py \
-       --data-dir /path/to/dataset \
-       --model Qwen/Qwen2-VL-7B-Instruct \
-       --sample-interval 5.0 \
-       --output-dir ./outputs/quick_test
-   ```
-
-2. **Verify output quality:**
-   ```bash
-   head outputs/quick_test/meta/syn_annotations.jsonl
-   ```
-
-3. **Production run:**
-   ```bash
-   # Standard 1 second sampling for production
-   bash examples/dataset/run_pgen.sh
-   ```
-
-4. **Use in training:**
-   ```python
-   from lerobot.datasets.lerobot_dataset import LeRobotDataset
-   
-   ds = LeRobotDataset(repo_id="...", root="outputs/pgen_annotations")
-   
-   # Access high-level task for each frame
-   frame = ds[100]
-   task_idx = frame["task_index_high_level"].item()
-   ```
-
-## 📚 Documentation Files
-
-- **`README_PGEN.md`**: Full API reference and troubleshooting
-- **`example_pgen_usage.md`**: Practical examples with performance estimates
-- **`SAMPLING_DIAGRAM.md`**: Visual explanation of temporal sampling
-- **`PGEN_SUMMARY.md`**: This overview document
-
-## 🎯 Success Criteria
-
-✅ Script generates synthetic dialogue using Qwen VLM  
-✅ Adds `task_index_high_level` feature to dataset  
-✅ Saves tasks to `tasks_high_level.parquet`  
-✅ Implements efficient temporal sampling (30-100x speedup)  
-✅ Handles episode boundaries correctly  
-✅ Produces diverse interaction types (scenarios + responses)  
-✅ Maintains temporal coherence within episodes  
-✅ Includes comprehensive documentation and examples  
-✅ Ready for production use on real datasets  
-
-## 💡 Key Takeaway
-
-**The script processes ALL episodes with intelligent sampling:**
-- `--sample-interval` controls how often VLM is called (default: 1.0s)
-- ALL frames in ALL episodes get labeled (complete coverage)
-- Intermediate frames inherit from most recent sample (temporal coherence)
-- Achieves 30-100x speedup while maintaining quality
-- Adjust interval based on use case: 5.0s for testing, 1.0s for production, 0.5s for fine detail
-
-This makes the synthetic data generation **practical, scalable, and complete** for real-world datasets!
-

examples/dataset/README_PGEN.md

@@ -1,243 +0,0 @@
-# Synthetic Data Generation for Hierarchical Robot Policies
-
-This directory contains `annotate_pgen.py`, a script for generating synthetic user prompts and robot utterances for hierarchical policy training using Vision-Language Models (VLMs).
-
-## Overview
-
-The script implements the synthetic data generation pipeline described in the Hi-Robot paper:
-
-1. **Load** a LeRobot dataset with skill annotations (from `annotate.py`)
-2. **Generate** synthetic dialogue using Qwen VLM:
-   - User prompts (ℓ_t): Natural requests that lead to specific skills
-   - Robot utterances (u_t): Acknowledgments and clarifications
-3. **Save** results as a new dataset feature `task_index_high_level`
-
-## Prerequisites
-
-1. First, annotate your dataset with skills using `annotate.py`:
-
-```bash
-python examples/dataset/annotate.py \
-    --repo-id lerobot/svla_so101_pickplace \
-    --video-key observation.images.base \
-    --model Qwen/Qwen2-VL-7B-Instruct
-```
-
-This creates `meta/skills.json` with skill segmentation for each episode.
-
-## Usage
-
-### Basic Usage
-
-```bash
-python examples/dataset/annotate_pgen.py \
-    --repo-id lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --sample-interval 1.0 \
-    --output-dir ./outputs/pgen_dataset
-```
-
-**Note**: The script processes **all episodes** in the dataset. It generates dialogue every 1 second (`--sample-interval 1.0`) using temporal sampling. Frames between samples reuse the last generated dialogue. This makes the process efficient while ensuring complete dataset coverage.
-
-### Advanced Options
-
-```bash
-python examples/dataset/annotate_pgen.py \
-    --repo-id lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen3-VL-30B-A3B-Instruct \
-    --temperature 0.8 \
-    --sample-interval 0.5 \
-    --num-image-views-per-sample 2 \
-    --output-dir ./outputs/pgen_dataset \
-    --push-to-hub
-```
-
-This example uses a more powerful model and samples every 0.5 seconds for finer granularity.
-
-### Fast Testing (larger interval)
-
-```bash
-python examples/dataset/annotate_pgen.py \
-    --repo-id lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --sample-interval 5.0 \
-    --output-dir ./outputs/pgen_quick_test
-```
-
-Use a larger interval (5.0 seconds) for rapid iteration during development. All episodes are still processed.
-
-### Using Local Dataset
-
-```bash
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --output-dir ./outputs/pgen_dataset
-```
-
-## Output Files
-
-The script produces several outputs:
-
-1. **`meta/tasks_high_level.parquet`**: High-level tasks with user prompts and robot utterances
-   - Columns: task_index, user_prompt, robot_utterance, skill, scenario_type, response_type
-
-2. **`meta/syn_annotations.jsonl`**: Debug file with all generated dialogues
-   - One JSON object per line with full context for each frame
-
-3. **Modified dataset**: New dataset with `task_index_high_level` feature added to all parquet files
-
-## Scenario and Response Types
-
-The generator produces diverse interaction types:
-
-### Scenario Types
-- **specific_object**: Direct specification of objects/actions
-- **negative_task**: Instructions about what NOT to do
-- **situated_correction**: Adjustments based on current state
-- **implicit_request**: Implied needs without direct commands
-- **constraint_based**: Specific constraints or preferences
-
-### Response Types
-- **confirmation**: Simple acknowledgment ("OK, I'll do X")
-- **clarification**: Seeking confirmation ("Just to confirm...")
-- **acknowledgment**: Action acknowledgment ("Got it, doing X")
-- **constraint_acknowledgment**: Acknowledging constraints ("Sure, I'll X while Y")
-
-## Example Generated Data
-
-```json
-{
-  "episode_id": 0,
-  "frame_index": 45,
-  "timestamp": 2.5,
-  "skill_current": "robot arm picks up pink lego brick",
-  "skill_history": ["robot arm moves towards pink lego brick"],
-  "task_description": "pink lego brick into the transparent box",
-  "scenario_type": "specific_object",
-  "response_type": "confirmation",
-  "user_prompt": "Can you grab the pink brick?",
-  "robot_utterance": "Sure, I'll pick up the pink lego brick."
-}
-```
-
-## Accessing the Data
-
-After running the script, access the synthetic data in your code:
-
-```python
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-import pandas as pd
-
-# Load modified dataset
-dataset = LeRobotDataset(repo_id="lerobot/svla_so101_pickplace_with_high_level_tasks")
-
-# Access frame with high-level task
-frame = dataset[100]
-high_level_task_idx = frame["task_index_high_level"].item()
-
-# Load high-level tasks
-tasks_df = pd.read_parquet(dataset.root / "meta" / "tasks_high_level.parquet")
-task_info = tasks_df.iloc[high_level_task_idx]
-
-print(f"User prompt: {task_info['user_prompt']}")
-print(f"Robot utterance: {task_info['robot_utterance']}")
-print(f"Skill: {task_info['skill']}")
-```
-
-## Architecture
-
-The script is modular and extensible:
-
-```python
-# Core components
-class QwenPgen:
-    """VLM wrapper for generation"""
-    def call_qwen(images, prompt) -> dict
-    
-def construct_prompt(task, history, skill) -> str
-    """Build prompt for VLM"""
-    
-def annotate_sample(pgen, images, ...) -> dict
-    """Generate dialogue for one sample"""
-    
-def generate_synthetic_data(dataset, pgen, ...) -> tuple
-    """Process entire dataset"""
-```
-
-## Parameters
-
-| Parameter | Default | Description |
-|-----------|---------|-------------|
-| `--repo-id` | - | HuggingFace dataset ID |
-| `--data-dir` | - | Local dataset path |
-| `--model` | Qwen/Qwen2-VL-7B-Instruct | VLM model name |
-| `--device` | cuda | Device (cuda/cpu) |
-| `--dtype` | bfloat16 | Model precision |
-| `--temperature` | 0.7 | Sampling temperature |
-| `--sample-interval` | 1.0 | Generate dialogue every N seconds (all episodes processed) |
-| `--num-image-views-per-sample` | 1 | Number of cameras |
-| `--output-dir` | None | Output directory |
-| `--push-to-hub` | False | Push to HuggingFace Hub |
-
-## Sampling Strategy
-
-The script uses **temporal sampling** to efficiently generate dialogue:
-
-- **Default**: Generate dialogue every 1 second (`--sample-interval 1.0`)
-- **Efficiency**: If a dataset runs at 30fps, this samples ~3% of frames
-- **Propagation**: Frames between samples reuse the last generated task_index
-- **Episode-aware**: Always samples the first frame of each episode
-
-### Example with 30 fps dataset:
-```bash
-# Sample every 1 second (every 30 frames)
---sample-interval 1.0  # ~3,000 generations for a 100 episode dataset (3 sec/episode)
-
-# Sample every 0.5 seconds (every 15 frames)
---sample-interval 0.5  # ~6,000 generations (more granular)
-
-# Sample every 2 seconds (every 60 frames)
---sample-interval 2.0  # ~1,500 generations (more efficient)
-```
-
-### Why sampling works:
-- Skills typically last 1-3 seconds
-- Dialogue doesn't need to change every frame
-- Reduces computational cost by 30-100x
-- Still provides good coverage for training
-
-## Tips
-
-1. **Quick testing**: Use larger `--sample-interval` (e.g., 5.0 or 10.0) for rapid iteration
-2. **Monitor GPU**: VLM inference is memory-intensive
-3. **Check outputs**: Review `syn_annotations.jsonl` for quality
-4. **Adjust temperature**: Higher = more diverse, lower = more consistent
-5. **Multiple views**: Use `--num-image-views-per-sample 2+` for better context
-6. **Tune sampling**: Start with 1.0s, increase for speed (testing), decrease for granularity (production)
-
-## Troubleshooting
-
-### No skills.json found
-Run `annotate.py` first to generate skill annotations.
-
-### Out of memory
-- Reduce batch size to 1
-- Use smaller model (Qwen2-VL-7B instead of Qwen3-VL-30B)
-- Process fewer samples at a time
-
-### Poor quality generations
-- Adjust temperature (try 0.6-0.9)
-- Check that skills.json has good annotations
-- Ensure images are loading correctly
-
-## Citation
-
-Based on the Hi-Robot paper's synthetic data generation approach:
-```
-@article{hirobot2024,
-  title={Hi-Robot: Hierarchical Robot Learning with Vision-Language Models},
-  year={2024}
-}
-```
-

examples/dataset/SAMPLING_DIAGRAM.md

@@ -1,141 +0,0 @@
-# Temporal Sampling Strategy Visualization
-
-## How `--sample-interval` Works
-
-### Example: 30 fps dataset, `--sample-interval 1.0` (1 second)
-
-```
-Timeline (seconds):  0.0      0.5      1.0      1.5      2.0      2.5      3.0
-                     │        │        │        │        │        │        │
-Frames:              0───15───30───45───60───75───90───105──120──135──150
-                     │        │        │        │        │        │        │
-                     ▼                 ▼                 ▼                 ▼
-Sampled:            YES      NO       YES      NO       YES      NO       YES
-                     │                 │                 │                 │
-Task Index:         [0]──────────────>[1]──────────────>[2]──────────────>[3]
-                     │                 │                 │                 │
-VLM Called:         ✓ Gen             ✓ Gen             ✓ Gen             ✓ Gen
-                    dialogue          dialogue          dialogue          dialogue
-                     │                 │                 │                 │
-Frames 0-29    ─────┘                 │                 │                 │
-get task 0                             │                 │                 │
-                                       │                 │                 │
-Frames 30-59  ────────────────────────┘                 │                 │
-get task 1                                               │                 │
-                                                         │                 │
-Frames 60-89  ──────────────────────────────────────────┘                 │
-get task 2                                                                 │
-                                                                           │
-Frames 90-119 ────────────────────────────────────────────────────────────┘
-get task 3
-```
-
-## Comparison: Different Sampling Intervals
-
-### `--sample-interval 2.0` (every 2 seconds)
-```
-Timeline:    0.0      1.0      2.0      3.0      4.0      5.0      6.0
-             │        │        │        │        │        │        │
-Sampled:    YES      NO       YES      NO       YES      NO       YES
-             │                 │                 │                 │
-Tasks:      [0]───────────────>[1]───────────────>[2]───────────────>[3]
-             
-VLM Calls:   4 (fewer calls, faster but less granular)
-```
-
-### `--sample-interval 1.0` (every 1 second) - **DEFAULT**
-```
-Timeline:    0.0   0.5   1.0   1.5   2.0   2.5   3.0   3.5   4.0   4.5   5.0   5.5   6.0
-             │     │     │     │     │     │     │     │     │     │     │     │     │
-Sampled:    YES   NO   YES   NO   YES   NO   YES   NO   YES   NO   YES   NO   YES
-             │           │           │           │           │           │           │
-Tasks:      [0]─────────>[1]─────────>[2]─────────>[3]─────────>[4]─────────>[5]─────>[6]
-             
-VLM Calls:   7 (balanced coverage and speed)
-```
-
-### `--sample-interval 0.5` (every 0.5 seconds)
-```
-Timeline:    0.0  0.5  1.0  1.5  2.0  2.5  3.0  3.5  4.0  4.5  5.0  5.5  6.0
-             │    │    │    │    │    │    │    │    │    │    │    │    │
-Sampled:    YES  YES  YES  YES  YES  YES  YES  YES  YES  YES  YES  YES  YES
-             │    │    │    │    │    │    │    │    │    │    │    │    │
-Tasks:      [0]─>[1]─>[2]─>[3]─>[4]─>[5]─>[6]─>[7]─>[8]─>[9]─>[10]>[11]>[12]
-             
-VLM Calls:   13 (high granularity, slower but more detailed)
-```
-
-## Episode Boundaries
-
-The script always samples the **first frame** of each episode:
-
-```
-Episode 0                          Episode 1                          Episode 2
-├─────────────────────────────────┤├─────────────────────────────────┤├──────...
-│                                 ││                                 ││
-Frame: 0    30    60    90   120  130   160   190   220  250  260   290  320
-Time:  0.0  1.0   2.0   3.0  4.0  0.0   1.0   2.0   3.0  4.0  0.0   1.0  2.0
-       │    │     │     │    │    │     │     │     │    │    │     │    │
-       ▼    ▼     ▼     ▼    ▼    ▼     ▼     ▼     ▼    ▼    ▼     ▼    ▼
-Sample:YES  YES   YES   YES  YES  YES   YES   YES   YES  YES  YES   YES  YES
-       │    │     │     │    │    │     │     │     │    │    │     │    │
-Task:  0────1─────2─────3────4    5─────6─────7─────8────9    10────11───12
-
-Note: Frames 0, 130, 260 are ALWAYS sampled (episode starts)
-      Even if they're within the sample-interval window
-```
-
-## Real-World Example: svla_so101_pickplace Dataset
-
-Typical stats:
-- **Total episodes**: 50
-- **Avg episode length**: 300 frames (10 seconds at 30 fps)
-- **Total frames**: 15,000
-
-### Without Sampling (every frame)
-```
-Frames processed:    15,000
-VLM calls:           15,000
-Time estimate:       ~5 hours
-Unique tasks:        ~12,000 (lots of duplicates)
-```
-
-### With `--sample-interval 1.0` (every 1 second)
-```
-Frames processed:    15,000 ✓
-VLM calls:           500
-Time estimate:       ~10 minutes
-Unique tasks:        ~450 (meaningful variety)
-Efficiency gain:     30x faster
-```
-
-### With `--sample-interval 2.0` (every 2 seconds)
-```
-Frames processed:    15,000 ✓
-VLM calls:           250
-Time estimate:       ~5 minutes
-Unique tasks:        ~220
-Efficiency gain:     60x faster
-```
-
-## Key Points
-
-1. **All frames get labeled**: Every frame gets a `task_index_high_level`
-2. **Only sampled frames call VLM**: Huge efficiency gain
-3. **Temporal coherence**: Nearby frames share the same task
-4. **Episode-aware**: Always samples episode starts
-5. **Configurable**: Adjust `--sample-interval` based on your needs
-
-## Choosing Your Sampling Interval
-
-| Use Case | Recommended Interval | Why |
-|----------|---------------------|-----|
-| Quick testing | 2.0s | Fastest iteration |
-| Standard training | 1.0s | Good balance |
-| High-quality dataset | 0.5s | Better coverage |
-| Fine-grained control | 0.33s | Very detailed |
-| Dense annotations | 0.1s | Nearly every frame |
-
-**Rule of thumb**: Match your sampling interval to your typical skill duration.
-If skills last 1-3 seconds, sampling every 1 second captures each skill multiple times.
-

examples/dataset/action_tokenizer_example.py

@@ -1,138 +0,0 @@
-#!/usr/bin/env python
-
-"""
-Example demonstrating how to use the ActionTokenizerProcessorStep to tokenize actions.
-
-This example shows how to:
-1. Load a dataset with action data
-2. Apply the action tokenizer processor to tokenize actions with proper padding/truncation
-3. Access both the tokenized actions and the attention mask
-4. Decode tokenized actions back to their original form
-"""
-
-import torch
-from transformers import AutoProcessor
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.processor.core import EnvTransition, TransitionKey
-from lerobot.processor.tokenizer_processor import ActionTokenizerProcessorStep
-from lerobot.utils.constants import ACTION_TOKEN_MASK
-
-# Define delta timestamps for the dataset
-delta_timestamps = {
-    'action': [
-        0.0, 0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333,
-        0.16666666666666666, 0.2, 0.23333333333333334, 0.26666666666666666, 0.3,
-        0.3333333333333333, 0.36666666666666664, 0.4, 0.43333333333333335,
-        0.4666666666666667, 0.5, 0.5333333333333333, 0.5666666666666667, 0.6,
-        0.6333333333333333, 0.6666666666666666, 0.7, 0.7333333333333333,
-        0.7666666666666667, 0.8, 0.8333333333333334, 0.8666666666666667, 0.9,
-        0.9333333333333333, 0.9666666666666667, 1.0, 1.0333333333333334,
-        1.0666666666666667, 1.1, 1.1333333333333333, 1.1666666666666667, 1.2,
-        1.2333333333333334, 1.2666666666666666, 1.3, 1.3333333333333333,
-        1.3666666666666667, 1.4, 1.4333333333333333, 1.4666666666666666, 1.5,
-        1.5333333333333334, 1.5666666666666667, 1.6, 1.6333333333333333
-    ]
-}
-
-# Load the dataset
-print("Loading dataset...")
-dataset = LeRobotDataset(
-    repo_id="local",
-    root="/fsx/jade_choghari/outputs/pgen_annotations1",
-    delta_timestamps=delta_timestamps
-)
-
-# Create a dataloader
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    num_workers=0,
-    batch_size=4,
-    shuffle=True,
-)
-
-# Get a batch of data
-batch = next(iter(dataloader))
-action_data = batch["action"]  # Shape: (batch_size, action_horizon, action_dim)
-
-print(f"\nOriginal action shape: {action_data.shape}")
-print(f"Original action data (first sample, first timestep):\n{action_data[0, 0]}")
-
-# Method 1: Using the tokenizer directly (as in fast_tokenize.py)
-print("\n" + "="*80)
-print("Method 1: Direct tokenizer usage")
-print("="*80)
-
-tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)
-
-# Tokenize directly
-tokens = tokenizer(action_data)
-print(f"\nDirect tokenization result type: {type(tokens)}")
-print(f"Tokens shape/length: {tokens.shape if isinstance(tokens, torch.Tensor) else len(tokens)}")
-
-# Decode
-decoded_actions = tokenizer.decode(tokens)
-print(f"Decoded actions shape: {decoded_actions.shape}")
-reconstruction_error = torch.abs(action_data - decoded_actions).mean()
-print(f"Mean absolute reconstruction error: {reconstruction_error.item():.6f}")
-
-# Method 2: Using the ActionTokenizerProcessorStep with proper padding/truncation
-print("\n" + "="*80)
-print("Method 2: Using ActionTokenizerProcessorStep (with padding & mask)")
-print("="*80)
-
-# Create the action tokenizer processor step
-action_tokenizer_processor = ActionTokenizerProcessorStep(
-    tokenizer_name="physical-intelligence/fast",
-    trust_remote_code=True,
-    max_action_tokens=32,  # Maximum number of tokens per action
-)
-
-# Create a transition with the action data
-transition = {
-    TransitionKey.ACTION: action_data,
-    TransitionKey.OBSERVATION: {},  # Empty for this example
-}
-
-# Apply the processor
-processed_transition = action_tokenizer_processor(transition)
-
-# Extract tokenized actions and mask
-tokenized_actions = processed_transition[TransitionKey.ACTION]
-complementary_data = processed_transition[TransitionKey.COMPLEMENTARY_DATA]
-action_mask = complementary_data[ACTION_TOKEN_MASK]
-
-print(f"\nTokenized actions shape: {tokenized_actions.shape}")  # (batch_size, max_action_tokens)
-print(f"Action mask shape: {action_mask.shape}")  # (batch_size, max_action_tokens)
-print(f"Tokenized actions dtype: {tokenized_actions.dtype}")
-print(f"Action mask dtype: {action_mask.dtype}")
-
-# Show token statistics
-print(f"\nFirst sample tokens: {tokenized_actions[0]}")
-print(f"First sample mask: {action_mask[0]}")
-num_real_tokens = action_mask[0].sum().item()
-print(f"Number of real tokens (non-padding): {num_real_tokens}")
-print(f"Number of padding tokens: {action_mask.shape[1] - num_real_tokens}")
-
-# Decode using the mask
-print("\nDecoding tokenized actions...")
-decoded_with_processor = tokenizer.decode(tokenized_actions)
-print(f"Decoded actions shape: {decoded_with_processor.shape}")
-
-# Calculate reconstruction error
-reconstruction_error_processor = torch.abs(action_data - decoded_with_processor).mean()
-print(f"Mean absolute reconstruction error: {reconstruction_error_processor.item():.6f}")
-
-# Show that masking works correctly
-print("\n" + "="*80)
-print("Mask demonstration")
-print("="*80)
-for i in range(min(4, tokenized_actions.shape[0])):
-    mask_i = action_mask[i]
-    num_real = mask_i.sum().item()
-    print(f"Sample {i}: {num_real} real tokens, {len(mask_i) - num_real} padding tokens")
-
-print("\n" + "="*80)
-print("Action tokenization example completed successfully!")
-print("="*80)
-

examples/dataset/example_pgen_usage.md

@@ -1,143 +0,0 @@
-# Example: Synthetic Data Generation with Sampling
-
-## Quick Start
-
-### 1. Test with 100 frames and 1 second sampling
-```bash
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --num-samples 100 \
-    --sample-interval 1.0 \
-    --output-dir ./outputs/test_pgen
-```
-
-**Expected behavior** (assuming 30 fps):
-- Total frames: 100
-- Frames sampled: ~4 (every 30 frames = 1 second)
-- Efficiency: 96% fewer VLM calls
-- Output: All 100 frames get `task_index_high_level`, but only 4 unique dialogues generated
-
-### 2. Process full dataset with different sampling rates
-
-#### Conservative (every 2 seconds)
-```bash
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --sample-interval 2.0 \
-    --output-dir ./outputs/pgen_2s
-```
-
-#### Standard (every 1 second) - **RECOMMENDED**
-```bash
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --sample-interval 1.0 \
-    --output-dir ./outputs/pgen_1s
-```
-
-#### Fine-grained (every 0.5 seconds)
-```bash
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen2-VL-7B-Instruct \
-    --sample-interval 0.5 \
-    --output-dir ./outputs/pgen_0.5s
-```
-
-## Performance Estimates
-
-For a dataset with:
-- 100 episodes
-- 10 seconds per episode (average)
-- 30 fps
-- Total frames: 30,000
-
-| Sampling Interval | Frames Sampled | % Sampled | Speedup | Time Estimate |
-|-------------------|----------------|-----------|---------|---------------|
-| Every frame (0.033s) | 30,000 | 100% | 1x | ~10 hours |
-| 0.5 seconds | 2,000 | 6.7% | 15x | ~40 min |
-| **1.0 seconds** | **1,000** | **3.3%** | **30x** | **~20 min** |
-| 2.0 seconds | 500 | 1.7% | 60x | ~10 min |
-
-*Note: Times are approximate and depend on GPU, model size, and generation speed*
-
-## Understanding the Output
-
-### Console Output Example
-```
-[cyan]Generating synthetic data for 30000 frames...[/cyan]
-[cyan]Sampling interval: 1.0s (fps: 30)[/cyan]
-Generating synthetic dialogue: 100%|████████| 30000/30000 [20:15<00:00, 24.68it/s]
-[green]✓ Sampled 1000 frames out of 30000 (3.3%)[/green]
-[green]✓ Generated 450 unique high-level tasks[/green]
-```
-
-### What happens:
-1. **Frame 0 (t=0.0s)**: Generate dialogue → Task index 0
-2. **Frames 1-29 (t=0.033s-0.967s)**: Reuse task index 0
-3. **Frame 30 (t=1.0s)**: Generate new dialogue → Task index 1
-4. **Frames 31-59 (t=1.033s-1.967s)**: Reuse task index 1
-5. And so on...
-
-### Result:
-- Every frame has a `task_index_high_level`
-- Only sampled frames have unique dialogues generated
-- Intermediate frames inherit from the most recent sample
-- Maintains temporal coherence within episodes
-
-## Checking Your Results
-
-After running, verify the output:
-
-```bash
-# Check the generated tasks
-python -c "
-import pandas as pd
-from pathlib import Path
-
-tasks = pd.read_parquet('outputs/test_pgen/meta/tasks_high_level.parquet')
-print(f'Total unique tasks: {len(tasks)}')
-print(f'Sample tasks:')
-print(tasks[['user_prompt', 'robot_utterance', 'skill']].head())
-"
-
-# Check debug output
-head outputs/test_pgen/meta/syn_annotations.jsonl
-
-# Load and verify dataset
-python -c "
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-ds = LeRobotDataset(repo_id='local_with_high_level_tasks', 
-                    root='outputs/test_pgen')
-print(f'Dataset has {len(ds)} frames')
-print(f'Features: {list(ds.features.keys())}')
-assert 'task_index_high_level' in ds.features
-print('✓ task_index_high_level feature added successfully!')
-"
-```
-
-## Common Use Cases
-
-### Development/Testing
-```bash
---sample-interval 2.0  # Fast iteration
---num-samples 500      # Small subset
-```
-
-### Production Training
-```bash
---sample-interval 1.0  # Good coverage
-# Process all samples (no --num-samples)
-```
-
-### High-Quality Dataset
-```bash
---sample-interval 0.5  # Fine-grained
---temperature 0.6      # More consistent
---model Qwen/Qwen3-VL-30B-A3B-Instruct  # Larger model
-```
-

examples/dataset/fast_tokenize.py

@@ -1,25 +0,0 @@
-import numpy as np
-from transformers import AutoProcessor
-import torch
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-
-delta_timestamps = {'action': [0.0, 0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333, 0.16666666666666666, 0.2, 0.23333333333333334, 0.26666666666666666, 0.3, 0.3333333333333333, 0.36666666666666664, 0.4, 0.43333333333333335, 0.4666666666666667, 0.5, 0.5333333333333333, 0.5666666666666667, 0.6, 0.6333333333333333, 0.6666666666666666, 0.7, 0.7333333333333333, 0.7666666666666667, 0.8, 0.8333333333333334, 0.8666666666666667, 0.9, 0.9333333333333333, 0.9666666666666667, 1.0, 1.0333333333333334, 1.0666666666666667, 1.1, 1.1333333333333333, 1.1666666666666667, 1.2, 1.2333333333333334, 1.2666666666666666, 1.3, 1.3333333333333333, 1.3666666666666667, 1.4, 1.4333333333333333, 1.4666666666666666, 1.5, 1.5333333333333334, 1.5666666666666667, 1.6, 1.6333333333333333]}
-dataset = LeRobotDataset(repo_id="local", root="/fsx/jade_choghari/outputs/pgen_annotations1", delta_timestamps=delta_timestamps)
-
-dataloader = torch.utils.data.DataLoader(
-        dataset,
-        num_workers=0,
-        batch_size=4,
-        shuffle=True,
-)
-
-batch = next(iter(dataloader))
-
-# Load the tokenizer from the Hugging Face hub
-tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)
-
-# Tokenize & decode action chunks (we use dummy data here)
-action_data = batch["action"]    # one batch of action chunks
-tokens = tokenizer(action_data)              # tokens = list[int]
-decoded_actions = tokenizer.decode(tokens)
-print("tokenized actions: ", tokens)

examples/dataset/inference_gemma.py

@@ -1,17 +0,0 @@
-from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
-
-model_id = "google/paligemma-3b-pt-224"
-model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
-processor = AutoProcessor.from_pretrained(model_id)
-
-breakpoint()
-prefix_output = model.language_model.forward(
-    inputs_embeds=inputs_embeds[0],
-    attention_mask=attention_mask,
-    position_ids=position_ids,
-    adarms_cond=adarms_cond[0] if adarms_cond is not None else None,
-)
-prefix_past_key_values = prefix_output.past_key_values
-# prefix_output to be used for the language head
-# shape: [batch_size, seq_len, hidden_size] with hidden_size = 2048
-prefix_output = prefix_output.last_hidden_state

examples/dataset/inference_pi05.py

@@ -1,91 +0,0 @@
-import torch
-from huggingface_hub import HfApi
-
-import lerobot
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-# import make_pre_post_processors
-from lerobot.policies.factory import make_pre_post_processors
-from lerobot.policies.pi05.configuration_pi05 import PI05Config
-from lerobot.policies.factory import make_policy, make_policy_config
-from lerobot.configs.policies import PreTrainedConfig
-
-cfg = PreTrainedConfig.from_pretrained(
-    pretrained_name_or_path="/fsx/jade_choghari/outputs/pi0_training/checkpoints/last/pretrained_model",
-)
-cfg.dtype = "bfloat16"
-
-pre_processor, post_processor = make_pre_post_processors(
-    policy_cfg=cfg,
-    pretrained_path="/fsx/jade_choghari/outputs/pi0_training/checkpoints/last/pretrained_model",
-)
-
-delta_timestamps = {'action': [0.0, 0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333, 0.16666666666666666, 0.2, 0.23333333333333334, 0.26666666666666666, 0.3, 0.3333333333333333, 0.36666666666666664, 0.4, 0.43333333333333335, 0.4666666666666667, 0.5, 0.5333333333333333, 0.5666666666666667, 0.6, 0.6333333333333333, 0.6666666666666666, 0.7, 0.7333333333333333, 0.7666666666666667, 0.8, 0.8333333333333334, 0.8666666666666667, 0.9, 0.9333333333333333, 0.9666666666666667, 1.0, 1.0333333333333334, 1.0666666666666667, 1.1, 1.1333333333333333, 1.1666666666666667, 1.2, 1.2333333333333334, 1.2666666666666666, 1.3, 1.3333333333333333, 1.3666666666666667, 1.4, 1.4333333333333333, 1.4666666666666666, 1.5, 1.5333333333333334, 1.5666666666666667, 1.6, 1.6333333333333333]}
-
-dataset = LeRobotDataset(repo_id="local", root="/fsx/jade_choghari/outputs/pgen_annotations1", delta_timestamps=delta_timestamps)
-
-# rename map --rename_map='{
-#         "observation.images.side": "observation.images.base_0_rgb",
-#         "observation.images.up": "observation.images.left_wrist_0_rgb"
-#         }'
-rename_map = {
-    "observation.images.side": "observation.images.base_0_rgb",
-    "observation.images.up": "observation.images.left_wrist_0_rgb"
-}
-policy = make_policy(
-    cfg=cfg,
-    ds_meta=dataset.meta,
-    rename_map=rename_map,
-)
-
-dataloader = torch.utils.data.DataLoader(
-        dataset,
-        num_workers=0,
-        batch_size=4,
-        shuffle=True,
-)
-
-batch = next(iter(dataloader))
-batch = pre_processor(batch)
-policy.train()
-# run inference
-# action = policy.select_action(batch)
-loss, loss_dict = policy.forward(batch)
-breakpoint()
-# import requests
-# from PIL import Image
-# from transformers import AutoProcessor
-# model = policy.model.paligemma_with_expert.paligemma
-# model = model.to(device="cuda", dtype=torch.bfloat16)
-# model.eval()
-# prompt = "Describe this image."
-# url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
-# image = Image.open(requests.get(url, stream=True).raw)
-# processor = AutoProcessor.from_pretrained(
-#     "google/paligemma-3b-pt-224",
-# )
-# inputs = processor(image, prompt, return_tensors="pt").to(model.device)
-# print("generating...")
-# output = model.generate(
-#     **inputs,
-#     max_new_tokens=50,
-#     use_cache=True,  # default dynamic cache
-# )
-# print(processor.decode(output[0], skip_special_tokens=True))
-
-
-# # other model
-# from transformers import PaliGemmaForConditionalGeneration
-# model = PaliGemmaForConditionalGeneration.from_pretrained(
-#     "google/paligemma2-3b-pt-224",
-#     torch_dtype=torch.bfloat16,
-#     device_map="auto",
-# )
-# model.eval()
-# print("generating...")
-# output = model.generate(
-#     **inputs,
-#     max_new_tokens=100,
-#     use_cache=True,  # default dynamic cache
-# )
-# print("Model 2 output:")
-# print(processor.decode(output[0], skip_special_tokens=True))

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/load_lerobot_high.py

@@ -1,23 +0,0 @@
-import torch
-from huggingface_hub import HfApi
-
-import lerobot
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-
-dataset = LeRobotDataset(repo_id="local", root="/fsx/jade_choghari/outputs/pgen_annotations1")
-
-dataloader = torch.utils.data.DataLoader(
-        dataset,
-        num_workers=0,
-        batch_size=32,
-        shuffle=True,
-)
-
-batch = next(iter(dataloader))
-print(batch.keys())
-print(batch['task_index_high_level'].shape)
-print(batch['task_index_high_level'])
-print(batch['user_prompt'][0])
-print(batch['robot_utterance'][0])
-print(batch['task'][0])
-breakpoint()

examples/dataset/load_libero.py

@@ -1,18 +0,0 @@
-import torch
-from huggingface_hub import HfApi
-
-import lerobot
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-
-dataset = LeRobotDataset(repo_id="lerobot/libero")
-
-dataloader = torch.utils.data.DataLoader(
-        dataset,
-        num_workers=0,
-        batch_size=4,
-        shuffle=True,
-)
-batch = next(iter(dataloader))
-print(batch.keys())
-
-breakpoint()

examples/dataset/mask.md

@@ -1,159 +0,0 @@
-## One-sentence answer
-
-> `make_att_2d_masks(prefix_pad_masks, prefix_att_masks)` builds the **actual 2D attention mask** `[B, L, L]` that tells the transformer **which token positions may attend to which others**, combining **padding** and **causality**.
-
-Everything else you’ve seen so far was just metadata.
-
----
-
-## What goes in
-
-### Inputs
-
-```python
-prefix_pad_masks   # shape [B, L]
-prefix_att_masks   # shape [B, L]
-```
-
-Where:
-
-* `prefix_pad_masks[b, i] = True`
-  → token `i` exists (not padding)
-
-* `prefix_att_masks[b, i] = False`
-  → token `i` is **bidirectional**
-
-* `prefix_att_masks[b, i] = True`
-  → token `i` is **causal (autoregressive)**
-
----
-
-## What comes out
-
-```python
-att_2d_prefix  # shape [B, L, L]
-```
-
-Each entry:
-
-```text
-att_2d_prefix[b, i, j] = True
-```
-
-means:
-
-> “In batch `b`, **token i (query)** is allowed to attend to **token j (key)**.”
-
----
-
-## How it is constructed (conceptually)
-
-For **each batch b**, **each query position i**, **each key position j**:
-
-```python
-if not prefix_pad_masks[b, j]:
-    att[b, i, j] = False           # cannot attend to padding
-else if not prefix_att_masks[b, i]:
-    att[b, i, j] = True            # bidirectional token → can see all real tokens
-else:
-    att[b, i, j] = (j <= i)        # causal token → can see only past + itself
-```
-
-That’s it.
-
----
-
-## Tiny concrete example (exactly matching your code)
-
-Suppose:
-
-```python
-prefix_pad_masks[0] = [T, T, T, T, T, F]
-prefix_att_masks[0] = [F, F, F, T, T, T]
-```
-
-Tokens:
-
-```
-0: IMG
-1: IMG
-2: LANG
-3: SUB0
-4: SUB1
-5: PAD
-```
-
----
-
-### Resulting `att_2d_prefix[0]`
-
-`✓ = True, ✗ = False`
-
-| Q \ K      | 0 | 1 | 2 | 3 | 4 | 5 |
-| ---------- | - | - | - | - | - | - |
-| 0 (bi)     | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
-| 1 (bi)     | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
-| 2 (bi)     | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
-| 3 (causal) | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ |
-| 4 (causal) | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
-| 5 (pad)    | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
-
----
-
-## Why this matters for your training code
-
-This line:
-
-```python
-att_2d_prefix_4d = self._prepare_attention_masks_4d(att_2d_prefix)
-```
-
-Converts `[B, L, L] → [B, 1, L, L]` and possibly flips True/False to `0/-inf`.
-
-This is **exactly what Paligemma uses inside self-attention**.
-
----
-
-## Key implications (VERY important)
-
-### 1️⃣ This mask does **not isolate token groups**
-
-* Bidirectional tokens can attend to **everything**
-* Causal tokens only restrict *their own row*
-
-So **flow/action tokens must be blocked separately**.
-
----
-
-### 2️⃣ This is why your AR subtask prediction works
-
-* Subtask tokens are causal
-* Output at position `i` predicts token `i+1`
-* Padding is fully ignored
-
----
-
-### 3️⃣ Inference behavior
-
-When `subtask_tokens = None`:
-
-* `prefix_att_masks` contains only `False`
-* `att_2d_prefix` becomes **fully bidirectional**
-* No AR behavior remains
-
-Exactly what you want.
-
----
-
-## One-sentence takeaway (commit this)
-
-> `make_att_2d_masks` fuses **padding** and **causality** into a concrete `[B, L, L]` attention matrix that the transformer actually uses.
-
-If you want next, I can:
-
-* inspect `make_att_2d_masks()` source with you
-* show how to block **flow → subtask** attention
-* explain how this changes when suffix tokens are added
-* help you refactor this into a cleaner “grouped attention” API
-
-You’re now at the point where the model’s behavior should feel *predictable*, not magical.

examples/dataset/prompt.txt

@@ -1,334 +0,0 @@
-Generate annotate_pgen.py using Qwen for synthetic data generation
-
-You are writing a Python script called annotate_pgen.py.
-This script generates synthetic user prompts (ℓ_t) and robot utterances (u_t) for Hi Robot–style hierarchical policy training, using Qwen 3vl as the generator model (pgen).
-
-SCRIPT PURPOSE
-
-The script must:
-
-Load Dlabeled which is a LeRobot Dataset that has been annotate using the annotate.py script, which contains:
-
-images: list of image paths at time t
-
-skill_current: the annotated skill label (ℓ̂_t)
-
-skill_history: list of previous skill labels (ℓ̂₀ … ℓ̂_{t−1}), those where annotated, and you can find details on them stored in teh dataset inside the the DATA_PATH/meta/skills.json
-
-you will find something like 
-
-{
-  "coarse_description": "pink lego brick into the transparent box",
-  "skill_to_task_index": {
-    "robot arm picks up pink lego brick": 19,
-    "robot arm approaches transparent box": 3,
-    "robot arm retracts from transparent box": 28,
-    "robot arm moves towards pink lego brick": 12,
-    "robot arm releases red lego brick into box": 26,
-    "robot arm releases red lego brick into transparent box": 27,
-    "robot arm closes gripper to pick up the pink lego brick": 5,
-    "robot arm lifts the pink lego brick": 7,
-    etc..
-  },
-  "episodes": {
-    "0": {
-      "episode_index": 0,
-      "description": "pink lego brick into the transparent box",
-      "skills": [
-        {
-          "name": "robot arm moves towards pink lego brick",
-          "start": 0.0,
-          "end": 1.8
-        },
-        {
-          "name": "robot arm picks up pink lego brick",
-          "start": 1.8,
-          "end": 3.1
-        },
-        {
-          "name": "robot arm moves towards transparent box",
-          "start": 3.1,
-          "end": 5.5
-        },
-        {
-          "name": "robot arm releases pink lego brick into transparent box",
-          "start": 5.5,
-          "end": 7.0
-        },
-        {
-          "name": "robot arm retracts from transparent box",
-          "start": 7.0,
-          "end": 10.1
-        }
-      ]
-    },
-    "1": {
-      "episode_index": 1,
-      "description": "pink lego brick into the transparent box",
-      "skills": [
-        {
-          "name": "robot arm moves towards red lego brick",
-          "start": 0.0,
-          "end": 1.2
-        },
-        {
-          "name": "robot arm picks up red lego brick",
-          "start": 1.2,
-          "end": 2.0
-        },
-        {
-          "name": "robot arm moves towards transparent box",
-          "start": 2.0,
-          "end": 3.8
-        },
-        {
-          "name": "robot arm places red lego brick into transparent box",
-          "start": 3.8,
-          "end": 5.0
-        },
-        {
-          "name": "robot arm moves away from transparent box",
-          "start": 5.0,
-          "end": 8.9
-        }
-      ]
-    },
-
-notice how task_description: is a high-level description (e.g., "make a sandwich") stored in description for each episode
-
-For each sample, call Qwen VLM to generate:
-
-synthetic user prompt ℓ_t
-
-synthetic robot response u_t
-
-Save results to D_syn in Parquet format insdie DATA_PATH/meta/tasks.parquet ; note tasks.parquet already contains the other tasks, so you need to update
-
-Should be modular, clean, easy to extend, with:
-
-a PGEN_PROMPT_TEMPLATE
-
-a construct_prompt() method
-
-a call_qwen() method
-
-a annotate_sample() method
-
-a CLI entrypoint (if __name__ == "__main__":)
-
-📦 INPUT FORMAT (Dlabeled)
-
-The script should expect Dlabeled as a .jsonl file where each line has:
-
-{
-  "episode_id": "ep_001",
-  "t": 37,
-  "images": ["path/to/cam0_t.jpg", "path/to/cam1_t.jpg"],
-  "skill_current": "pick up the KitKat",
-  "skill_history": ["open fridge", "pick up lettuce", "place lettuce"],
-  "task_description": "making a sandwich"
-}
-
-📤 OUTPUT FORMAT (D_syn)
-
-Each line of synthetically generated data should be:
-
-{
-  "episode_id": "ep_001",
-  "t": 37,
-  "images": ["path/to/cam0_t.jpg", "path/to/cam1_t.jpg"],
-  "skill_current": "pick up the KitKat",
-  "skill_history": [...],
-  "user_prompt": "Can you grab me something sweet?",
-  "robot_utterance": "Sure, I can pick up the KitKat.",
-  "task_description": "making a sandwich"
-}
-
-
-Store as syn_annotations.jsonl. for debugging
-
-🧠 pgen MODEL (Qwen) REQUIREMENTS
-
-Use HuggingFace Transformers:
-
-Qwen/Qwen2-VL-7B-Instruct (or any Qwen2-VL Vision-Language model available)
-
-Use the image + text chat interface
-
-Vision inputs should be loaded with PIL
-
-Use a single forward pass that outputs BOTH ℓ_t and u_t in a structured JSON
-
-📝 PROMPT FORMAT FOR pgen
-
-Create a template like:
-
-You are a robot-assistant dialogue generator for hierarchical robot policies.
-
-You will receive:
-- A list of images showing the current robot scene.
-- The high-level task: {task_description}
-- Previous skill steps completed: {skill_history}
-- The next skill to be performed by the robot: {skill_current}
-
-Generate two things in JSON:
-1. "user_prompt": a natural-sounding user request that logically leads to the robot performing the skill "{skill_current}" given the task and history.
-2. "robot_utterance": a natural robot reply acknowledging or clarifying the request.
-
-The responses must be grounded in the visual scene, the task, and the skill history.
-
-Respond ONLY in JSON:
-{
-  "user_prompt": "...",
-  "robot_utterance": "..."
-}
-
-This resposne will have a corresponsing task_index, and the task will be saved in task.parqeut and you must update each dataset parquet in for example /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace/data/chunk-000/
-file-000.parquet to include this new feature called task_index_high_level consider udpatign the metadata in info.json as well
-📌 LOGIC REQUIRED
-construct_prompt(sample)
-
-Loads sample dict
-
-Inserts:
-
-task_description
-
-skill_history
-
-skill_current
-
-Returns a full text prompt string
-
-call_qwen(images, prompt)
-
-Loads images into Qwen-VL multimodal input format
-
-Calls model.generate
-
-Parses JSON output
-
-annotate_sample(sample)
-
-Builds prompt
-
-Calls Qwen
-
-Returns augmented sample with user_prompt + robot_utterance
-
-🚀 CLI Usage
-
-The script should run as:
-
-python annotate_pgen.py \
-  --output-dir PATH \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --repo-id lerobot/svla_so101_pickplace \
-  --model Qwen/Qwen3-VL-30B-A3B-Instruct \
-  --batch-size 1
-
-
-Include arguments via argparse.
-
-🔧 OTHER REQUIREMENTS
-
-Use tqdm for progress bars
-
-Log errors gracefully and continue
-
-Support GPU acceleration (device="cuda")
-
-Cache model loading so it's not reloaded every call
-
-Make the prompt deterministic but allow temperature parameter
-
-Add a flag --num-image-views-per-sample
-
-Add automatic JSON parsing with helpful error messages
-
-🎯 FINAL DELIVERABLE
-
-Cursor must now generate:
-A full Python file named annotate_pgen.py implementing the above functionality end-to-end.
-
-It should be production-ready, runnable on real data, cleanly structured, and easy to modify.
-
-
-from the paper:
-Next, we use a large vision-language model (VLM) pgen
-to produce synthetic user prompts and interjections ℓt,
-and corresponding robot utterance ut. Given Dlabeled, we
-prompt pgen with both the visual context I1
-t ,...,In
-t and the
-skill labelˆ
-ℓt (e.g., pick up the lettuce). pgen then imag-
-ines an appropriate interaction that might have led toˆ
-ℓt in a
-real user interaction: it generates possible user prompts ℓt
-(e.g., “Can you add some lettuce for me?”) along with the
-robot’s verbal responses and clarifications ut. We detail the
-A. Synthetic Data Generation
-A.1. Scenario and Response Categorization
-To ensure the quality and diversity of the synthetic data,
-we incorporate structured scenario classification and re-
-sponse categorization into the prompt design for pgen, fol-
-lowing (Stephan et al., 2024). Specifically, we classify
-interactions into different scenario types, such as nega-
-tive task (where the user instructs the robot what not to
-do), situated correction (where the user adjusts an earlier
-command based on the evolving task state), and specific
-constraint (where the user specifies particular constraints,
-such as dietary preferences). In addition, we categorize
-the robot’s responses into types such as simple confirma-
-tions, clarifications, and error handling. These classifica-
-tions guide the generation process to ensure a broad range
-of user-robot interactions.
-A.2. Prompt Construction for Contextual Grounding
-In prompt P, we include a detailed description of the task
-(e.g., bussing a table, making a sandwich, grocery shop-
-ping) and instruct the model to ground responses in visual
-observations and prior context. A key advantage of lever-
-aging large pretrained VLMs is their ability to incorporate
-world knowledge when generating interactions. For in-
-stance, the model can infer dietary constraints when gener-
-ating prompts for sandwich-making, producing user com-
-mands such as “Can you make a sandwich for me? I’m
-lactose intolerant” and an appropriate robot response like
-“Sure, I won’t put cheese on it.” Similarly, it can reason
-over ambiguous or implicit requests, such as inferring that
-“I want something sweet” in a grocery shopping scenario
-should lead to suggestions like chocolate or candy.
-To maintain consistency in multi-step tasks, we condition
-pgen on prior skill labels within an episodeˆ
-ˆ
-ℓ0,...,
-ℓt−1,
-allowing it to generate coherent user commands that
-account for past actions. For instance, if the robot
-has already placed lettuce and tomato on a sandwich,
-the generated user prompt might request additional in-
-gredients that logically follow. This ensures that the
-synthetic interactions reflect realistic task progression
-rather than isolated commands. As such, we leverage
-ˆ
-ˆ
-ˆ
-pgen(ℓt,ut|I1
-t ,...,In
-t ,
-ℓ0,...,
-ℓt−1,
-ℓt,P) to produce a richer,
-more diverse synthetic dataset Dsyn that provides mean-
-ingful supervision for training our high-level policy.
-While in this work we generate a separate Dsyn and train
-a separate high-level policy for each task (e.g., sandwich
-making vs. table cleaning) for clarity and ease of bench-
-marking, the architecture is readily amenable to a unified
-multi-task formulation. In principle, the same hierarchical
-approach could be used to train a single high-level policy
-across a multitude of tasks, facilitating knowledge transfer
-
-
-The result should be a new LeRobotDataset with a new feature called task_index_high_level inside each dataset parquet

examples/dataset/run.sh

@@ -1,11 +0,0 @@
-python examples/dataset/annotate.py \
-    --repo-id jadechoghari/collect-data \
-    --video-key observation.images.base \
-    --model Qwen/Qwen3-VL-30B-A3B-Instruct \
-    --episodes 16 22
-
-# python examples/dataset/annotate.py \
-#     --repo-id lerobot/svla_so101_pickplace \
-#     --video-key observation.images.side \
-#     --model Qwen/Qwen3-VL-30B-A3B-Instruct \
-#     --episodes 5

examples/dataset/run_pgen.sh

@@ -1,43 +0,0 @@
-#!/bin/bash
-
-# Example script to run synthetic data generation with Qwen VLM
-# This generates user prompts and robot utterances for hierarchical policy training
-
-# Configuration
-REPO_ID="jadechoghari/collect-data"
-MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct"
-# Alternative: MODEL="Qwen/Qwen2-VL-7B-Instruct"
-
-
-OUTPUT_DIR="/fsx/jade_choghari/outputs/collect-data-pgen"
-BATCH_SIZE=32
-TEMPERATURE=0.9
-SAMPLE_INTERVAL=5.0  # Generate dialogue every 1 second (all episodes processed)
-
-# Run synthetic data generation (processes ALL episodes)
-python examples/dataset/annotate_pgen.py \
-    --repo-id "$REPO_ID" \
-    --model "$MODEL" \
-    --output-dir "$OUTPUT_DIR" \
-    --temperature "$TEMPERATURE" \
-    --batch-size "$BATCH_SIZE" \
-    --sample-interval "$SAMPLE_INTERVAL" \
-    --image-key observation.images.base \
-    --num-image-views-per-sample 1
-
-# For faster testing, increase sample interval:
-# --sample-interval 5.0  # Samples every 5 seconds (much faster)
-
-# To push to hub after generation:
-# Add --push-to-hub flag
-
-# Efficient batch processing: 4 episodes at once
-# python examples/dataset/annotate_pgen.py \
-#     --repo-id "$REPO_ID" \
-#     --model "$MODEL" \
-#     --output-dir "$OUTPUT_DIR" \
-#     --video-mode \
-#     --video-key observation.images.up \
-#     --video-batch-size "$BATCH_SIZE" \
-#     --sample-interval 1.0
-

examples/dataset/subtask_annotation.py

@@ -1,802 +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.
-
-"""
-SARM Subtask Annotation using local GPU (Qwen3-VL).
-
-This script implements the annotation approach from the SARM paper using local GPU inference:
-"SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation"
-Paper: https://arxiv.org/pdf/2509.25358
-
-What it does:
-1. Takes videos from a LeRobot dataset
-2. Uses Qwen3-VL running locally on GPU to identify when subtasks occur
-3. Saves subtask timestamps to the dataset metadata
-4. Optionally pushes the annotated dataset to HuggingFace Hub
-
-SARM trains reward models that predict:
-  - Stage: Which subtask is currently being executed (discrete classification)
-  - Progress: How far along the subtask we are (continuous 0-1)
-
-Supports three annotation modes:
-  1. No annotations (no args): Auto-creates single sparse "task" stage covering full episode.
-     Use with SARM config annotation_mode="single_stage" for simple tasks.
-
-  2. Dense-only (--dense-only --dense-subtasks): Dense annotations from VLM, auto-generated
-     single sparse "task" stage. Use with annotation_mode="dense_only".
-
-  3. Dual mode (--sparse-subtasks + --dense-subtasks): Both sparse and dense annotations
-     from VLM. Use with annotation_mode="dual".
-
-Requirements:
-  - GPU with sufficient VRAM (16GB+ recommended for 30B model)
-  - `pip install transformers, torch, qwen-vl-utils`
-
-Run with:
-```bash
-python examples/dataset_annotation/subtask_annotation.py \
-  --repo-id your-username/your-dataset \
-  --sparse-subtasks "Do ..." \
-  --dense-subtasks "Do task 1, Do task 2, Do task 3" \
-  --video-key observation.images.base \
-  --push-to-hub
-```
-"""
-
-import argparse
-import json
-import multiprocessing as mp
-import re
-import subprocess
-import tempfile
-import textwrap
-import time
-from concurrent.futures import ProcessPoolExecutor, as_completed
-from pathlib import Path
-
-import cv2
-import pandas as pd
-import torch
-from qwen_vl_utils import process_vision_info
-from rich.console import Console
-from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.policies.sarm.sarm_utils import (
-    Subtask,
-    SubtaskAnnotation,
-    Timestamp,
-    compute_temporal_proportions,
-)
-
-
-def create_sarm_prompt(subtask_list: list[str]) -> str:
-    subtask_str = "\n".join([f"  - {name}" for name in subtask_list])
-
-    return textwrap.dedent(f"""\
-        # Role
-        You are a Robotics Vision System specializing in temporal action localization for robot manipulation. Your job is to segment a single demonstration video into distinct, non-overlapping atomic actions from a fixed subtask list.
-
-        # Subtask Label Set (Closed Vocabulary)
-        You must strictly identify the video segments using ONLY the following labels. Do not create new labels or modify existing ones:
-
-        [
-        {subtask_str}
-        ]
-
-        The video shows one successful execution of all subtasks in a logical order.
-
-        # Ground-Truth Semantics (Very Important)
-        Use **visual state changes** to define when a subtask starts and ends. Do NOT assume equal durations for the subtasks.
-
-        - A subtask **starts** at the first frame where the robot's motion clearly initiates that subtask.
-        - A subtask **ends** at the first frame where that specific action is visually completed and the manipulated object reaches a temporary, stable configuration.
-
-        If there are short pauses or micro-motions that don't clearly correspond to a new subtask, they belong to the **current** subtask.
-
-        # Hard Constraints & Logic
-        1. **Continuous Coverage (No Gaps):**
-           - The entire video duration from "00:00" to the final timestamp must be covered by subtasks.
-           - There can be no gaps between subtasks.
-           - If there is any idle or ambiguous time between clear actions, extend the *preceding* subtask to cover it.
-
-        2. **Boundary Consistency:**
-           - The `"end"` timestamp of one subtask must be exactly equal to the `"start"` timestamp of the next subtask.
-           - Boundaries must coincide with a real visual state transition, not just a convenient time split.
-
-        3. **Chronological Order, One Occurrence Each:**
-           - This is a single successful demonstration.
-           - Each subtask from the vocabulary appears **exactly once**, in the correct logical order.
-           - **Durations may be very different** between subtasks. Never assume they are similar lengths. Base all boundaries only on the video.
-
-        4. **Reject Uniform Segmentation (Important):**
-           - Do NOT simply divide the video into equal or nearly equal time chunks.
-           - If your boundaries would result in subtasks with similar durations (e.g. all around 5 seconds), treat this as evidence that your segmentation is wrong and refine the boundaries.
-           - Only use nearly equal durations if the video truly shows each subtask taking the same amount of time (this is very rare).
-
-        5. **Timestamps:**
-           - Timestamps must be in `"MM:SS"` format.
-           - The first subtask always starts at `"00:00"`.
-           - The last subtask ends at the final visible frame of the video.
-
-        # Step 1 — Textual Timeline (must do this first)
-        First, write a extensive and detailed textual timeline describing what happens in the video with approximate timestamps.
-        For each subtask, include:
-        - its name
-        - an approximate start and end time,
-        - an description of the visual event at the boundary (e.g. "shirt fully folded to the left", "robot rotates folded shirt 90 degrees").
-
-        Format this as a bullet list.
-
-        # Step 2 — JSON Output (final answer)
-        After the textual timeline, output **only** valid JSON with this structure.
-        The JSON **must** be consistent with the textual timeline above:
-
-        {{
-          "subtasks": [
-            {{
-              "name": "EXACT_NAME_FROM_LIST",
-              "timestamps": {{
-                "start": "MM:SS",
-                "end":   "MM:SS"
-              }}
-            }},
-            {{
-              "name": "EXACT_NAME_FROM_LIST",
-              "timestamps": {{
-                "start": "MM:SS",
-                "end":   "MM:SS"
-              }}
-            }}
-          ]
-        }}
-
-        Do not add any extra keys to the JSON.
-        """)
-
-
-class VideoAnnotator:
-    """Annotates robot manipulation videos using local Qwen3-VL model on GPU"""
-
-    def __init__(
-        self,
-        subtask_list: list[str],
-        model_name: str = "Qwen/Qwen3-VL-30B-A3B-Instruct",
-        device: str = "cuda",
-        torch_dtype: torch.dtype = torch.bfloat16,
-        model: "Qwen3VLMoeForConditionalGeneration | None" = None,
-        processor: "AutoProcessor | None" = None,
-    ):
-        """
-        Initialize the video annotator with local model.
-
-        Args:
-            subtask_list: List of allowed subtask names (for consistency)
-            model_name: Hugging Face model name (default: Qwen/Qwen3-VL-30B-A3B-Instruct)
-            device: Device to use (cuda, cpu)
-            torch_dtype: Data type for model (bfloat16, float16, float32)
-            model: Pre-loaded model instance (optional, to share between annotators)
-            processor: Pre-loaded processor instance (optional, to share between annotators)
-        """
-        self.subtask_list = subtask_list
-        self.prompt = create_sarm_prompt(subtask_list)
-        self.console = Console()
-        self.device = device
-
-        # Use provided model/processor or load new ones
-        if model is not None and processor is not None:
-            self.model = model
-            self.processor = processor
-            self.console.print(f"[green]✓ Using shared model on {device}[/green]")
-        else:
-            self.console.print(f"[cyan]Loading model: {model_name}...[/cyan]")
-
-            self.model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
-                model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True
-            )
-
-            self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
-
-            self.console.print(f"[green]✓ Model loaded successfully on {device}[/green]")
-
-    def extract_episode_segment(
-        self, file_path: Path, start_timestamp: float, end_timestamp: float, target_fps: int = 1
-    ) -> Path:
-        """
-        Extract a specific episode segment from concatenated video.
-        Uses minimal compression to preserve quality for local inference.
-
-        Args:
-            file_path: Path to the concatenated video file
-            start_timestamp: Starting timestamp in seconds (within this video file)
-            end_timestamp: Ending timestamp in seconds (within this video file)
-            target_fps: Target FPS (default: 1 for faster processing)
-
-        Returns:
-            Path to extracted video file
-        """
-        # Create temporary file for extracted video
-        tmp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-        tmp_path = Path(tmp_file.name)
-        tmp_file.close()
-
-        try:
-            # Check if ffmpeg is available
-            subprocess.run(
-                ["ffmpeg", "-version"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True
-            )
-        except (subprocess.CalledProcessError, FileNotFoundError):
-            raise RuntimeError("ffmpeg not found, cannot extract episode segment") from e
-
-        try:
-            # Calculate duration
-            duration = end_timestamp - start_timestamp
-
-            self.console.print(
-                f"[cyan]Extracting episode: {start_timestamp:.1f}s-{end_timestamp:.1f}s ({duration:.1f}s)[/cyan]"
-            )
-
-            # Use ffmpeg to extract segment with minimal quality loss
-            cmd = [
-                "ffmpeg",
-                "-i",
-                str(file_path),
-                "-ss",
-                str(start_timestamp),
-                "-t",
-                str(duration),
-                "-r",
-                str(target_fps),
-                "-c:v",
-                "libx264",
-                "-preset",
-                "ultrafast",
-                "-crf",
-                "23",
-                "-an",
-                "-y",
-                str(tmp_path),
-            ]
-
-            subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True)
-
-            # Verify the output file was created and is not empty
-            if not tmp_path.exists() or tmp_path.stat().st_size == 0:
-                self.console.print("[red]✗ Video extraction failed (0 bytes) - skipping episode[/red]")
-                if tmp_path.exists():
-                    tmp_path.unlink()
-                raise RuntimeError("FFmpeg produced empty video file")
-
-            # Show extraction results
-            file_size_mb = tmp_path.stat().st_size / (1024 * 1024)
-
-            # Fail if file is too small (< 100KB likely means extraction failed)
-            if file_size_mb < 0.1:
-                self.console.print(
-                    f"[red]✗ Extracted video too small ({file_size_mb:.2f}MB) - skipping episode[/red]"
-                )
-                tmp_path.unlink()
-                raise RuntimeError(f"Video extraction produced invalid file ({file_size_mb:.2f}MB)")
-
-            self.console.print(f"[green]✓ Extracted: {file_size_mb:.1f}MB ({target_fps} FPS)[/green]")
-
-            return tmp_path
-
-        except subprocess.CalledProcessError as e:
-            raise RuntimeError(f"ffmpeg failed ({e})") from e
-
-    def annotate(
-        self,
-        file_path: str | Path,
-        fps: int,
-        start_timestamp: float = 0.0,
-        end_timestamp: float | None = None,
-        max_retries: int = 3,
-    ) -> SubtaskAnnotation:
-        """Annotate a video segment using local GPU."""
-        file_path = Path(file_path)
-
-        if end_timestamp is None:
-            cap = cv2.VideoCapture(str(file_path))
-            end_timestamp = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) / (cap.get(cv2.CAP_PROP_FPS) or 1)
-            cap.release()
-
-        duration = end_timestamp - start_timestamp
-        duration_str = f"{int(duration // 60):02d}:{int(duration % 60):02d}"
-
-        extracted_path = self.extract_episode_segment(file_path, start_timestamp, end_timestamp, 1)
-        is_extracted = extracted_path != file_path
-
-        try:
-            messages = [
-                {"role": "system", "content": [{"type": "text", "text": self.prompt}]},
-                {
-                    "role": "user",
-                    "content": [
-                        {"type": "video", "video": str(extracted_path), "fps": 1.0},
-                        {
-                            "type": "text",
-                            "text": f"Video is {duration_str} (~{duration:.1f}s). Follow instructions.",
-                        },
-                    ],
-                },
-            ]
-
-            for attempt in range(max_retries):
-                try:
-                    text = self.processor.apply_chat_template(
-                        messages, tokenize=False, add_generation_prompt=True
-                    )
-                    image_inputs, video_inputs = process_vision_info(messages)
-                    inputs = self.processor(
-                        text=[text],
-                        images=image_inputs,
-                        videos=video_inputs,
-                        padding=True,
-                        return_tensors="pt",
-                    ).to(self.device)
-
-                    with torch.no_grad():
-                        generated_ids = self.model.generate(
-                            **inputs, max_new_tokens=1024, do_sample=True, temperature=0.7
-                        )
-
-                    response = self.processor.batch_decode(
-                        [out[len(inp) :] for inp, out in zip(inputs.input_ids, generated_ids)],
-                        skip_special_tokens=True,
-                    )[0].strip()
-
-                    # Extract JSON
-                    if "```json" in response:
-                        response = response.split("```json")[1].split("```")[0]
-                    elif "```" in response:
-                        response = response.split("```")[1].split("```")[0]
-
-                    try:
-                        return SubtaskAnnotation.model_validate(json.loads(response))
-                    except json.JSONDecodeError:
-                        match = re.search(r"\{.*\}", response, re.DOTALL)
-                        if match:
-                            return SubtaskAnnotation.model_validate(json.loads(match.group()))
-                        raise ValueError("No JSON found")
-                except Exception as e:
-                    if attempt == max_retries - 1:
-                        raise RuntimeError(f"Failed after {max_retries} attempts") from e
-                    time.sleep(1)
-        finally:
-            if is_extracted and extracted_path.exists():
-                extracted_path.unlink()
-
-
-def display_annotation(
-    annotation: SubtaskAnnotation, console: Console, episode_idx: int, fps: int, prefix: str = ""
-):
-    """Display annotation summary."""
-    subtask_summary = ", ".join(
-        f"{s.name}({s.timestamps.start}-{s.timestamps.end})" for s in annotation.subtasks
-    )
-    console.print(
-        f"[green]Episode {episode_idx} {prefix}: {len(annotation.subtasks)} subtasks - {subtask_summary}[/green]"
-    )
-
-
-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 save_annotations_to_dataset(
-    dataset_path: Path, annotations: dict[int, SubtaskAnnotation], fps: int, prefix: str = "sparse"
-):
-    """Save annotations to LeRobot dataset parquet format."""
-    from lerobot.datasets.utils import DEFAULT_EPISODES_PATH, load_episodes
-
-    episodes_dataset = load_episodes(dataset_path)
-    if not episodes_dataset or len(episodes_dataset) == 0:
-        return
-
-    episodes_df = episodes_dataset.to_pandas()
-    cols = [
-        f"{prefix}_{c}"
-        for c in [
-            "subtask_names",
-            "subtask_start_times",
-            "subtask_end_times",
-            "subtask_start_frames",
-            "subtask_end_frames",
-        ]
-    ]
-    for col in cols:
-        episodes_df[col] = None
-
-    for ep_idx, ann in annotations.items():
-        if ep_idx >= len(episodes_df):
-            continue
-        names, starts, ends, start_frames, end_frames = [], [], [], [], []
-        for s in ann.subtasks:
-            names.append(s.name)
-            st, et = timestamp_to_seconds(s.timestamps.start), timestamp_to_seconds(s.timestamps.end)
-            starts.append(st)
-            ends.append(et)
-            start_frames.append(int(st * fps))
-            end_frames.append(int(et * fps))
-        episodes_df.at[ep_idx, cols[0]] = names
-        episodes_df.at[ep_idx, cols[1]] = starts
-        episodes_df.at[ep_idx, cols[2]] = ends
-        episodes_df.at[ep_idx, cols[3]] = start_frames
-        episodes_df.at[ep_idx, cols[4]] = end_frames
-
-    # Group by file and write
-    for ep_idx in episodes_df.index:
-        key = (
-            episodes_df.loc[ep_idx, "meta/episodes/chunk_index"],
-            episodes_df.loc[ep_idx, "meta/episodes/file_index"],
-        )
-        path = dataset_path / DEFAULT_EPISODES_PATH.format(chunk_index=key[0], file_index=key[1])
-        if path.exists():
-            file_df = pd.read_parquet(path)
-            for col in cols + (
-                [
-                    "subtask_names",
-                    "subtask_start_times",
-                    "subtask_end_times",
-                    "subtask_start_frames",
-                    "subtask_end_frames",
-                ]
-                if prefix == "sparse"
-                else []
-            ):
-                if col not in file_df.columns:
-                    file_df[col] = None
-            if ep_idx in annotations:
-                for col in cols:
-                    file_df.at[ep_idx, col] = episodes_df.loc[ep_idx, col]
-                if prefix == "sparse":  # Legacy columns
-                    for i, legacy in enumerate(
-                        [
-                            "subtask_names",
-                            "subtask_start_times",
-                            "subtask_end_times",
-                            "subtask_start_frames",
-                            "subtask_end_frames",
-                        ]
-                    ):
-                        file_df.at[ep_idx, legacy] = episodes_df.loc[ep_idx, cols[i]]
-            file_df.to_parquet(path, engine="pyarrow", compression="snappy")
-
-
-def generate_auto_sparse_annotations(
-    dataset: LeRobotDataset, episode_indices: list[int], video_key: str
-) -> dict[int, SubtaskAnnotation]:
-    """Auto-generate single 'task' stage annotations for all episodes."""
-    annotations = {}
-    for ep_idx in episode_indices:
-        start = float(dataset.meta.episodes[f"videos/{video_key}/from_timestamp"][ep_idx])
-        end = float(dataset.meta.episodes[f"videos/{video_key}/to_timestamp"][ep_idx])
-        duration = end - start
-        end_str = f"{int(duration // 60):02d}:{int(duration % 60):02d}"
-        annotations[ep_idx] = SubtaskAnnotation(
-            subtasks=[Subtask(name="task", timestamps=Timestamp(start="00:00", end=end_str))]
-        )
-    return annotations
-
-
-def load_annotations_from_dataset(dataset_path: Path, prefix: str = "sparse") -> dict[int, SubtaskAnnotation]:
-    """Load annotations from LeRobot dataset parquet files."""
-    from lerobot.datasets.utils import load_episodes
-
-    episodes_dataset = load_episodes(dataset_path)
-    if not episodes_dataset or len(episodes_dataset) == 0:
-        return {}
-
-    col_names = f"{prefix}_subtask_names"
-    col_start = f"{prefix}_subtask_start_times"
-    col_end = f"{prefix}_subtask_end_times"
-
-    # Fall back to legacy columns for sparse
-    if col_names not in episodes_dataset.column_names:
-        if prefix == "sparse" and "subtask_names" in episodes_dataset.column_names:
-            col_names, col_start, col_end = "subtask_names", "subtask_start_times", "subtask_end_times"
-        else:
-            return {}
-
-    df = episodes_dataset.to_pandas()
-    annotations = {}
-    for ep_idx in df.index:
-        names = df.loc[ep_idx, col_names]
-        if names is None or (isinstance(names, float) and pd.isna(names)):
-            continue
-        starts, ends = df.loc[ep_idx, col_start], df.loc[ep_idx, col_end]
-        annotations[int(ep_idx)] = SubtaskAnnotation(
-            subtasks=[
-                Subtask(
-                    name=n,
-                    timestamps=Timestamp(
-                        start=f"{int(s) // 60:02d}:{int(s) % 60:02d}",
-                        end=f"{int(e) // 60:02d}:{int(e) % 60:02d}",
-                    ),
-                )
-                for n, s, e in zip(names, starts, ends)
-            ]
-        )
-    return annotations
-
-
-def process_single_episode(
-    ep_idx: int,
-    dataset_root: Path,
-    dataset_meta,
-    video_key: str,
-    fps: int,
-    annotator: VideoAnnotator,
-    console: Console,
-) -> tuple[int, SubtaskAnnotation | None, str | None]:
-    """Process a single episode annotation."""
-    try:
-        video_path = dataset_root / dataset_meta.get_video_file_path(ep_idx, video_key)
-        if not video_path.exists():
-            return ep_idx, None, f"Video not found: {video_path}"
-
-        start = float(dataset_meta.episodes[f"videos/{video_key}/from_timestamp"][ep_idx])
-        end = float(dataset_meta.episodes[f"videos/{video_key}/to_timestamp"][ep_idx])
-        return ep_idx, annotator.annotate(video_path, fps, start, end), None
-    except Exception as e:
-        return ep_idx, None, str(e)
-
-
-def worker_process_episodes(
-    worker_id: int,
-    gpu_id: int,
-    episode_indices: list[int],
-    repo_id: str,
-    video_key: str,
-    sparse_subtask_list: list[str],
-    dense_subtask_list: list[str] | None,
-    model_name: str,
-    torch_dtype: torch.dtype,
-) -> tuple[dict, dict | None]:
-    """Worker for parallel processing across GPUs."""
-    device = f"cuda:{gpu_id}"
-    console = Console()
-    dataset = LeRobotDataset(repo_id, download_videos=False)
-
-    sparse_annotator = VideoAnnotator(sparse_subtask_list, model_name, device, torch_dtype)
-    dense_annotator = (
-        VideoAnnotator(
-            dense_subtask_list,
-            model_name,
-            device,
-            torch_dtype,
-            sparse_annotator.model,
-            sparse_annotator.processor,
-        )
-        if dense_subtask_list
-        else None
-    )
-
-    sparse_annotations, dense_annotations = {}, {} if dense_subtask_list else None
-
-    for ep_idx in episode_indices:
-        _, sparse_ann, err = process_single_episode(
-            ep_idx, dataset.root, dataset.meta, video_key, dataset.fps, sparse_annotator, console
-        )
-        if sparse_ann:
-            sparse_annotations[ep_idx] = sparse_ann
-
-        if dense_annotator:
-            _, dense_ann, _ = process_single_episode(
-                ep_idx, dataset.root, dataset.meta, video_key, dataset.fps, dense_annotator, console
-            )
-            if dense_ann:
-                dense_annotations[ep_idx] = dense_ann
-
-    return sparse_annotations, dense_annotations
-
-
-def main():
-    parser = argparse.ArgumentParser(description="SARM-style subtask annotation using local GPU (Qwen3-VL)")
-    parser.add_argument("--repo-id", type=str, required=True, help="HuggingFace dataset repository ID")
-    parser.add_argument(
-        "--sparse-subtasks", type=str, default=None, help="Comma-separated sparse subtask names"
-    )
-    parser.add_argument(
-        "--dense-subtasks", type=str, default=None, help="Comma-separated dense subtask names"
-    )
-    parser.add_argument(
-        "--dense-only", action="store_true", help="Dense-only mode with auto-generated sparse 'task' stage"
-    )
-    parser.add_argument("--episodes", type=int, nargs="+", default=None, help="Episode indices to annotate")
-    parser.add_argument("--model", type=str, default="Qwen/Qwen3-VL-30B-A3B-Instruct", help="VLM model")
-    parser.add_argument("--skip-existing", action="store_true", help="Skip already annotated episodes")
-    parser.add_argument("--video-key", type=str, default=None, help="Video key (default: first available)")
-    parser.add_argument("--push-to-hub", action="store_true", help="Push to HuggingFace Hub")
-    parser.add_argument("--output-repo-id", type=str, default=None, help="Output repo ID for push")
-    parser.add_argument("--device", type=str, default="cuda", help="Device (cuda/cpu)")
-    parser.add_argument("--dtype", type=str, default="bfloat16", choices=["bfloat16", "float16", "float32"])
-    parser.add_argument("--num-workers", type=int, default=1, help="Parallel workers for multi-GPU")
-    parser.add_argument("--gpu-ids", type=int, nargs="+", default=None, help="GPU IDs to use")
-
-    args = parser.parse_args()
-    console = Console()
-
-    # Validate arguments
-    if args.dense_only and not args.dense_subtasks:
-        return console.print("[red]Error: --dense-only requires --dense-subtasks[/red]")
-    if args.dense_subtasks and not args.sparse_subtasks and not args.dense_only:
-        return console.print("[red]Error: --dense-subtasks requires --sparse-subtasks or --dense-only[/red]")
-
-    sparse_subtask_list = (
-        [s.strip() for s in args.sparse_subtasks.split(",")] if args.sparse_subtasks else None
-    )
-    dense_subtask_list = [s.strip() for s in args.dense_subtasks.split(",")] if args.dense_subtasks else None
-    auto_sparse = sparse_subtask_list is None
-    dense_mode = dense_subtask_list is not None
-    torch_dtype = {"bfloat16": torch.bfloat16, "float16": torch.float16, "float32": torch.float32}[args.dtype]
-
-    console.print(f"[cyan]Loading dataset: {args.repo_id}[/cyan]")
-    dataset = LeRobotDataset(args.repo_id, download_videos=True)
-    fps = dataset.fps
-
-    if not dataset.meta.video_keys:
-        raise ValueError("No video keys found")
-
-    video_key = (
-        args.video_key if args.video_key in (dataset.meta.video_keys or []) else dataset.meta.video_keys[0]
-    )
-    console.print(f"[cyan]Using camera: {video_key}, FPS: {fps}[/cyan]")
-
-    # Determine episodes
-    episode_indices = args.episodes or list(range(dataset.meta.total_episodes))
-
-    existing_annotations = load_annotations_from_dataset(dataset.root, prefix="sparse")
-    if args.skip_existing:
-        episode_indices = [ep for ep in episode_indices if ep not in existing_annotations]
-
-    if not episode_indices:
-        return console.print("[green]All episodes already annotated![/green]")
-    console.print(f"[cyan]Annotating {len(episode_indices)} episodes[/cyan]")
-
-    # GPU setup
-    gpu_ids = args.gpu_ids or list(
-        range(min(args.num_workers, torch.cuda.device_count() if torch.cuda.is_available() else 1))
-    )
-    args.num_workers = len(gpu_ids)
-
-    sparse_annotations = existing_annotations.copy()
-    dense_annotations = {} if dense_mode else None
-
-    # Auto-sparse mode
-    if auto_sparse:
-        sparse_annotations.update(generate_auto_sparse_annotations(dataset, episode_indices, video_key))
-        save_annotations_to_dataset(dataset.root, sparse_annotations, fps, prefix="sparse")
-        console.print(f"[green]Auto-generated {len(episode_indices)} sparse 'task' annotations[/green]")
-
-    # VLM annotation (for sparse if not auto, and for dense)
-    need_vlm = (not auto_sparse) or dense_mode
-
-    if need_vlm:
-        if args.num_workers > 1 and not auto_sparse:
-            # Parallel processing
-            console.print(f"[cyan]Parallel processing with {args.num_workers} workers[/cyan]")
-            episodes_per_worker = [[] for _ in range(args.num_workers)]
-            for i, ep_idx in enumerate(episode_indices):
-                episodes_per_worker[i % args.num_workers].append(ep_idx)
-
-            with ProcessPoolExecutor(
-                max_workers=args.num_workers, mp_context=mp.get_context("spawn")
-            ) as executor:
-                futures = [
-                    executor.submit(
-                        worker_process_episodes,
-                        w,
-                        gpu_ids[w],
-                        episodes_per_worker[w],
-                        args.repo_id,
-                        video_key,
-                        sparse_subtask_list,
-                        dense_subtask_list,
-                        args.model,
-                        torch_dtype,
-                    )
-                    for w in range(args.num_workers)
-                    if episodes_per_worker[w]
-                ]
-
-                for future in as_completed(futures):
-                    try:
-                        worker_sparse, worker_dense = future.result()
-                        sparse_annotations.update(worker_sparse)
-                        if dense_mode and worker_dense:
-                            dense_annotations.update(worker_dense)
-                        save_annotations_to_dataset(dataset.root, sparse_annotations, fps, prefix="sparse")
-                        if dense_mode:
-                            save_annotations_to_dataset(dataset.root, dense_annotations, fps, prefix="dense")
-                    except Exception as e:
-                        raise RuntimeError(f"Worker failed: {e}") from e
-        else:
-            # Sequential processing
-            sparse_annotator = (
-                VideoAnnotator(sparse_subtask_list, args.model, args.device, torch_dtype)
-                if not auto_sparse and sparse_subtask_list
-                else None
-            )
-            dense_annotator = (
-                VideoAnnotator(
-                    dense_subtask_list,
-                    args.model,
-                    args.device,
-                    torch_dtype,
-                    sparse_annotator.model if sparse_annotator else None,
-                    sparse_annotator.processor if sparse_annotator else None,
-                )
-                if dense_mode
-                else None
-            )
-
-            for i, ep_idx in enumerate(episode_indices):
-                console.print(f"[cyan]Episode {ep_idx} ({i + 1}/{len(episode_indices)})[/cyan]")
-
-                if sparse_annotator:
-                    _, sparse_ann, err = process_single_episode(
-                        ep_idx, dataset.root, dataset.meta, video_key, fps, sparse_annotator, console
-                    )
-                    if sparse_ann:
-                        sparse_annotations[ep_idx] = sparse_ann
-                        save_annotations_to_dataset(dataset.root, sparse_annotations, fps, prefix="sparse")
-                    elif err:
-                        console.print(f"[red]Sparse failed: {err}[/red]")
-
-                if dense_annotator:
-                    _, dense_ann, err = process_single_episode(
-                        ep_idx, dataset.root, dataset.meta, video_key, fps, dense_annotator, console
-                    )
-                    if dense_ann:
-                        dense_annotations[ep_idx] = dense_ann
-                        save_annotations_to_dataset(dataset.root, dense_annotations, fps, prefix="dense")
-                    elif err:
-                        console.print(f"[red]Dense failed: {err}[/red]")
-
-    # Save temporal proportions
-    def save_proportions(annotations, prefix, is_auto=False):
-        props: dict[str, float] = {"task": 1.0} if is_auto else compute_temporal_proportions(annotations, fps)
-        path = dataset.root / "meta" / f"temporal_proportions_{prefix}.json"
-        path.parent.mkdir(parents=True, exist_ok=True)
-        with open(path, "w") as f:
-            json.dump(props, f, indent=2)
-        console.print(f"[green]Saved {prefix} temporal proportions[/green]")
-
-    save_proportions(sparse_annotations, "sparse", auto_sparse)
-    if dense_mode and dense_annotations:
-        save_proportions(dense_annotations, "dense")
-
-    console.print(
-        f"\n[bold green]Complete! {len(sparse_annotations)} sparse, {len(dense_annotations or {})} dense annotations[/bold green]"
-    )
-
-    if args.push_to_hub:
-        try:
-            dataset.push_to_hub(push_videos=True)
-            console.print(f"[green]Pushed to {args.output_repo_id or args.repo_id}[/green]")
-        except Exception as e:
-            console.print(f"[red]Push failed: {e}[/red]")
-
-
-if __name__ == "__main__":
-    main()

examples/dataset/test.txt

@@ -1 +0,0 @@
-srun --time 12:00:00     --qos=high     --gres=gpu:1     --mem=24G     --partition=hopper-prod     --container-image /fsx/michel_aractingi/docker_images/huggingface+lerobot-gpu+dev.sqsh     --container-mounts /fsx/jade_choghari   

examples/dataset/test_pgen_quick.sh

@@ -1,44 +0,0 @@
-#!/bin/bash
-
-# Quick test to verify the fix for task_indices length mismatch
-# This should now work correctly even with --num-samples < full dataset length
-
-echo "Testing annotate_pgen.py with --num-samples=100 on full dataset..."
-
-python examples/dataset/annotate_pgen.py \
-    --data-dir /fsx/jade_choghari/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace \
-    --model Qwen/Qwen3-VL-30B-A3B-Instruct \
-    --num-samples 100 \
-    --sample-interval 1.0 \
-    --output-dir /fsx/jade_choghari/outputs/pgen_test_fixed
-
-if [ $? -eq 0 ]; then
-    echo "✓ SUCCESS: Script completed without errors!"
-    echo ""
-    echo "Verifying output..."
-    
-    # Check that all frames have task_index_high_level
-    python -c "
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-import numpy as np
-
-ds = LeRobotDataset(repo_id='local_test', root='/fsx/jade_choghari/outputs/pgen_test_fixed')
-print(f'Dataset has {len(ds)} frames')
-print(f'Features: {list(ds.features.keys())}')
-
-# Check that task_index_high_level exists
-assert 'task_index_high_level' in ds.features, 'task_index_high_level not in features!'
-
-# Sample some frames
-for idx in [0, 50, 99, 100, 500, 1000, 11938]:
-    if idx < len(ds):
-        frame = ds[idx]
-        task_idx = frame['task_index_high_level'].item()
-        print(f'Frame {idx}: task_index_high_level = {task_idx}')
-
-print('✓ All checks passed!')
-"
-else
-    echo "✗ FAILED: Script exited with error code $?"
-fi
-

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!")

examples/voice_control/README.md

@@ -1,47 +0,0 @@
-# Voice Assistant Examples
-
-Voice-enabled robot assistant examples using speech-to-text (STT), and text-to-speech (TTS).
-
-## Overview
-
-These examples demonstrate how to build a voice interface for robot control:
-
-1. **Hold SPACE** → Push-to-talk recording starts
-2. **Release SPACE** → Recording stops
-3. **STT (Whisper)** → Converts speech to text (high-level task prompt)
-4. **Pi0.5** → Generates robot response/utterance
-5. **TTS (Kokoro)** → Speaks the response back
-
-## Requirements
-
-```bash
-pip install torch transformers sounddevice numpy pynput kokoro>=0.9.2
-```
-
-## Usage
-
-### With Pi0.5 Model
-
-```bash
-python examples/voice_assistant/voice_assistant_pi05.py \
-    --pretrained_path path/to/pi05/checkpoint
-```
-
-## How It Works
-
-### Pi0.5 Voice Integration
-
-Pi0.5 can generate robot utterances as part of its subtask prediction. The flow:
-
-1. **High-level prompt**: User voice command is transcribed and formatted as a task prompt
-2. **Subtask generation**: Pi0.5 autoregressively generates a response
-3. **Utterance extraction**: If the response contains `<utterance>...</utterance>` tags, the content is extracted
-4. **TTS output**: The response is spoken back to the user
-
-## Configuration Options
-
-| Option | Default | Description |
-|--------|---------|-------------|
-| `--pretrained_path` | None | Path to Pi0.5 checkpoint |
-| `--record_seconds` | 5.0 | Audio recording duration |
-| `--max_response_tokens` | 100 | Max tokens in generated response |

examples/voice_control/voice_assistant_pi05.py

@@ -1,336 +0,0 @@
-#!/usr/bin/env python
-"""
-Voice Assistant with Pi0.5: Microphone → STT → Pi0.5 → TTS → Speaker
-
-This example demonstrates how to use Pi0.5 as a conversational robot assistant:
-1. Hold SPACE to record your voice command
-2. Speech-to-text (Whisper) converts speech to text
-3. Text is fed as a high-level prompt to Pi0.5
-4. Pi0.5 generates a response (robot utterance)
-5. Text-to-speech (Kokoro) speaks the response back
-
-Requirements:
-    pip install torch transformers sounddevice numpy pynput kokoro>=0.9.2
-
-Usage:
-    python examples/voice_assistant/voice_assistant_pi05.py \
-        --pretrained_path lerobot/pi0.5-base
-"""
-
-import os
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-import argparse
-import re
-import subprocess
-import threading
-import time
-
-import numpy as np
-import sounddevice as sd
-import torch
-from pynput import keyboard
-from transformers import AutoTokenizer, WhisperForConditionalGeneration, WhisperProcessor
-
-from lerobot.policies.pi05.configuration_pi05 import PI05Config
-from lerobot.policies.pi05.modeling_pi05 import PI05Pytorch
-
-SAMPLE_RATE = 16000
-
-
-def get_device():
-    if torch.cuda.is_available():
-        return torch.device("cuda")
-    elif torch.backends.mps.is_available():
-        return torch.device("mps")
-    return torch.device("cpu")
-
-
-class Pi05VoiceAssistant:
-    """Voice assistant using Pi0.5 for generating robot utterances."""
-
-    def __init__(
-        self,
-        pretrained_path: str | None = None,
-        max_response_tokens: int = 100,
-        max_record_seconds: float = 30.0,
-    ):
-        self.device = get_device()
-        self.dtype = torch.float32 if self.device.type == "mps" else torch.bfloat16
-        self.max_response_tokens = max_response_tokens
-        self.max_record_seconds = max_record_seconds
-
-        # Push-to-talk state
-        self._recording = False
-        self._audio_chunks: list[np.ndarray] = []
-        self._stream: sd.InputStream | None = None
-
-        print(f"Using device: {self.device}")
-        self._load_models(pretrained_path)
-
-    def _load_models(self, pretrained_path: str | None):
-        print("Loading STT (Whisper tiny)...")
-        self.stt_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
-        self.stt_model = WhisperForConditionalGeneration.from_pretrained(
-            "openai/whisper-tiny.en", torch_dtype=self.dtype
-        ).to(self.device)
-
-        print("Loading Pi0.5 model...")
-        self._load_pi05(pretrained_path)
-
-        print("Loading tokenizer...")
-        self.tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224")
-
-        self._load_tts()
-        print("Ready!\n")
-
-    def _load_pi05(self, pretrained_path: str | None):
-        """Load Pi0.5 model for utterance generation."""
-        config = PI05Config()
-        config.dtype = "float32" if self.device.type == "mps" else "bfloat16"
-
-        self.pi05_model = PI05Pytorch(config)
-
-        if pretrained_path:
-            try:
-                from safetensors.torch import load_file
-                state_dict = load_file(f"{pretrained_path}/model.safetensors")
-                self.pi05_model.load_state_dict(state_dict, strict=False)
-                print(f"✓ Loaded Pi0.5 weights from {pretrained_path}")
-            except Exception as e:
-                print(f"Warning: Could not load pretrained weights: {e}")
-                print("Using randomly initialized model for demo purposes")
-
-        self.pi05_model = self.pi05_model.to(self.device)
-        self.pi05_model.eval()
-
-    def _load_tts(self):
-        try:
-            print("Loading TTS (Kokoro 82M)...")
-            from kokoro import KPipeline
-
-            self.tts_pipeline = KPipeline(lang_code="a")  # American English
-            self.tts_voice = "af_heart"
-            self.tts_type = "kokoro"
-            print("Kokoro loaded!")
-        except Exception as e:
-            print(f"Kokoro not available ({e})")
-            print("Using macOS `say` for TTS")
-            self.tts_pipeline = None
-            self.tts_type = "system"
-
-    def _audio_callback(self, indata, frames, time_info, status):
-        """Callback for audio stream - collects chunks while recording."""
-        if self._recording:
-            self._audio_chunks.append(indata.copy())
-
-    def _start_recording(self):
-        """Start recording audio."""
-        if self._recording:
-            return
-        self._recording = True
-        self._audio_chunks = []
-        print("🎤 Recording... (release SPACE to stop)")
-
-    def _stop_recording(self) -> np.ndarray | None:
-        """Stop recording and return the audio."""
-        if not self._recording:
-            return None
-        self._recording = False
-
-        if not self._audio_chunks:
-            return None
-
-        audio = np.concatenate(self._audio_chunks, axis=0).flatten()
-        duration = len(audio) / SAMPLE_RATE
-        volume = np.abs(audio).max()
-        print(f"Recorded {duration:.1f}s, volume: {volume:.4f}")
-
-        if volume < 0.001:
-            print("⚠️  Very low audio - check microphone permissions!")
-            return None
-
-        return audio
-
-    def wait_for_spacebar(self) -> np.ndarray | None:
-        """Wait for spacebar press, record while held, return audio on release."""
-        audio_result = None
-        recording_done = threading.Event()
-
-        def on_press(key):
-            if key == keyboard.Key.space:
-                self._start_recording()
-
-        def on_release(key):
-            nonlocal audio_result
-            if key == keyboard.Key.space and self._recording:
-                audio_result = self._stop_recording()
-                recording_done.set()
-                return False  # Stop listener
-
-        # Start audio stream
-        self._stream = sd.InputStream(
-            samplerate=SAMPLE_RATE,
-            channels=1,
-            dtype="float32",
-            callback=self._audio_callback,
-            blocksize=int(SAMPLE_RATE * 0.1),  # 100ms blocks
-        )
-
-        with self._stream:
-            print("\n⏳ Press and hold SPACE to speak...")
-            with keyboard.Listener(on_press=on_press, on_release=on_release) as listener:
-                # Wait for recording to complete or timeout
-                recording_done.wait(timeout=self.max_record_seconds)
-                if self._recording:
-                    audio_result = self._stop_recording()
-
-        return audio_result
-
-    def transcribe(self, audio: np.ndarray) -> str:
-        start = time.perf_counter()
-        inputs = self.stt_processor(audio, sampling_rate=SAMPLE_RATE, return_tensors="pt")
-        input_features = inputs.input_features.to(self.device, dtype=self.dtype)
-        tokens = self.stt_model.generate(input_features)
-        text = self.stt_processor.batch_decode(tokens, skip_special_tokens=True)[0]
-        print(f"STT: {time.perf_counter() - start:.2f}s")
-        return text.strip()
-
-    def _create_dummy_images(self, batch_size: int = 1) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
-        """Create placeholder images for Pi0.5 when no camera is available."""
-        image_shape = (batch_size, 3, 224, 224)
-        dummy_image = torch.zeros(image_shape, dtype=torch.float32, device=self.device)
-        dummy_mask = torch.ones(batch_size, dtype=torch.bool, device=self.device)
-        return [dummy_image], [dummy_mask]
-
-    def _tokenize_prompt(self, text: str) -> tuple[torch.Tensor, torch.Tensor]:
-        """Tokenize the user prompt for Pi0.5."""
-        prompt = f"User request: {text}\nRobot response:"
-        tokenized = self.tokenizer(
-            [prompt],
-            max_length=200,
-            truncation=True,
-            padding="max_length",
-            return_tensors="pt",
-        )
-        tokens = tokenized["input_ids"].to(self.device)
-        masks = tokenized["attention_mask"].to(self.device, dtype=torch.bool)
-        return tokens, masks
-
-    def generate_response(self, user_text: str) -> str:
-        """Generate robot utterance using Pi0.5's language generation."""
-        start = time.perf_counter()
-
-        images, img_masks = self._create_dummy_images()
-        tokens, masks = self._tokenize_prompt(user_text)
-
-        with torch.no_grad():
-            generated_tokens = self.pi05_model._generate_subtask_tokens(
-                images=images,
-                img_masks=img_masks,
-                tokens=tokens,
-                masks=masks,
-                tokenizer=self.tokenizer,
-                max_length=self.max_response_tokens,
-                device=self.device,
-            )
-
-        # Decode generated tokens
-        valid_tokens = generated_tokens[0][generated_tokens[0] != 0]
-        response = self.tokenizer.decode(valid_tokens, skip_special_tokens=True)
-
-        # Extract utterance if marked with special tokens
-        response = self._extract_utterance(response)
-
-        print(f"Pi0.5: {time.perf_counter() - start:.2f}s")
-        return response.strip()
-
-    def _extract_utterance(self, text: str) -> str:
-        """Extract utterance from between <utterance> tokens if present."""
-        pattern = r"<utterance>(.*?)</utterance>"
-        match = re.search(pattern, text, re.DOTALL)
-        if match:
-            return match.group(1).strip()
-        return text
-
-    def speak(self, text: str):
-        start = time.perf_counter()
-        if self.tts_type == "kokoro":
-            generator = self.tts_pipeline(text, voice=self.tts_voice)
-            audio_chunks = [audio for _, _, audio in generator]
-            if audio_chunks:
-                audio = np.concatenate(audio_chunks)
-                sd.play(audio, 24000)
-                sd.wait()
-        else:
-            subprocess.run(["say", text], check=True)
-        print(f"TTS: {time.perf_counter() - start:.2f}s")
-
-    def run(self):
-        print("=" * 50)
-        print("Pi0.5 Voice Assistant")
-        print("=" * 50)
-        print("• Hold SPACE to record your voice command")
-        print("• Release SPACE when done speaking")
-        print("• Press Ctrl+C to exit")
-        print("=" * 50)
-
-        while True:
-            try:
-                audio = self.wait_for_spacebar()
-
-                if audio is None:
-                    print("(no audio captured)\n")
-                    continue
-
-                user_text = self.transcribe(audio)
-
-                if not user_text:
-                    print("(no speech detected)\n")
-                    continue
-
-                print(f"You: {user_text}")
-
-                response = self.generate_response(user_text)
-                print(f"Robot: {response}\n")
-
-                self.speak(response)
-
-            except KeyboardInterrupt:
-                print("\nGoodbye!")
-                break
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Pi0.5 Voice Assistant")
-    parser.add_argument(
-        "--pretrained_path",
-        type=str,
-        default=None,
-        help="Path to pretrained Pi0.5 model (optional)",
-    )
-    parser.add_argument(
-        "--max_response_tokens",
-        type=int,
-        default=100,
-        help="Maximum tokens in generated response",
-    )
-    parser.add_argument(
-        "--max_record_seconds",
-        type=float,
-        default=30.0,
-        help="Maximum recording duration in seconds",
-    )
-    args = parser.parse_args()
-
-    assistant = Pi05VoiceAssistant(
-        pretrained_path=args.pretrained_path,
-        max_response_tokens=args.max_response_tokens,
-        max_record_seconds=args.max_record_seconds,
-    )
-    assistant.run()
-
-
-if __name__ == "__main__":
-    main()

fast_tokenizer_local/metadata.json

@@ -1,27 +0,0 @@
-{
-  "repo_id": "local",
-  "vocab_size": 1024,
-  "scale": 10.0,
-  "encoded_dims": "0:7",
-  "encoded_dim_ranges": [
-    [
-      0,
-      7
-    ]
-  ],
-  "total_encoded_dims": 7,
-  "delta_dims": null,
-  "delta_dim_list": null,
-  "use_delta_transform": false,
-  "state_key": "observation.state",
-  "normalization_mode": "QUANTILES",
-  "action_horizon": 10,
-  "num_training_chunks": 25065,
-  "compression_stats": {
-    "compression_ratio": 3.464660463274599,
-    "mean_token_length": 20.204,
-    "p99_token_length": 36.00999999999999,
-    "min_token_length": 5.0,
-    "max_token_length": 38.0
-  }
-}

fast_tokenizer_local/processing_action_tokenizer.py

@@ -1,158 +0,0 @@
-import logging
-from typing import ClassVar
-
-import numpy as np
-from scipy.fft import dct
-from scipy.fft import idct
-from tokenizers import ByteLevelBPETokenizer
-from tokenizers.trainers import BpeTrainer
-from transformers import PreTrainedTokenizerFast
-from transformers.processing_utils import ProcessorMixin
-
-
-class UniversalActionProcessor(ProcessorMixin):
-    attributes: ClassVar[list[str]] = ["bpe_tokenizer"]
-    bpe_tokenizer_class: str = "AutoTokenizer"
-
-    def __init__(
-        self,
-        bpe_tokenizer: PreTrainedTokenizerFast,
-        scale: float = 10,
-        vocab_size: int = 1024,
-        min_token: int = 0,
-        *,
-        action_dim: int | None = None,
-        time_horizon: int | None = None,
-    ):
-        self.scale = scale
-        self.vocab_size = vocab_size
-        self.min_token = min_token
-
-        # Action horizon and dimension needed during decoding. These can be specified
-        # in three ways (in order of priority):
-        # 1. passed in as kwargs to decode()
-        # 2. in the constructor
-        # 3. cached from the last time decode() was called
-        self.time_horizon = time_horizon
-        self.action_dim = action_dim
-        self.called_time_horizon = time_horizon
-        self.called_action_dim = action_dim
-
-        super().__init__(bpe_tokenizer)
-
-    def __call__(self, action_chunk: np.array) -> np.array:
-        assert action_chunk.ndim <= 3, "Only 3 dimensions supported: [batch, timesteps, action_dim]"
-        if action_chunk.ndim == 2:
-            action_chunk = action_chunk[None, ...]
-
-        # Cache the time horizon and action dimension for decoding
-        self.called_time_horizon = action_chunk.shape[-2]
-        self.called_action_dim = action_chunk.shape[-1]
-
-        dct_coeff = dct(action_chunk, axis=1, norm="ortho")
-        dct_coeff = np.around(dct_coeff * self.scale)
-        tokens = []
-        for elem in dct_coeff:
-            token_str = "".join(map(chr, np.maximum(elem.flatten() - self.min_token, 0).astype(int)))
-            tokens.append(self.bpe_tokenizer(token_str)["input_ids"])
-        return tokens
-
-    def decode(
-        self,
-        tokens: list[list[int]],
-        *,
-        time_horizon: int | None = None,
-        action_dim: int | None = None,
-    ) -> np.array:
-        self.time_horizon = time_horizon or self.time_horizon or self.called_time_horizon
-        self.action_dim = action_dim or self.action_dim or self.called_action_dim
-
-        # Cache the time horizon and action dimension for the next call
-        self.called_time_horizon = self.time_horizon
-        self.called_action_dim = self.action_dim
-
-        assert (
-            self.time_horizon is not None and self.action_dim is not None
-        ), "Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim."
-
-        decoded_actions = []
-        for token in tokens:
-            try:
-                decoded_tokens = self.bpe_tokenizer.decode(token)
-                decoded_dct_coeff = np.array(list(map(ord, decoded_tokens))) + self.min_token
-                decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim)
-                assert (
-                    decoded_dct_coeff.shape
-                    == (
-                        self.time_horizon,
-                        self.action_dim,
-                    )
-                ), f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})"
-            except Exception as e:
-                print(f"Error decoding tokens: {e}")
-                print(f"Tokens: {token}")
-                decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim))
-            decoded_actions.append(idct(decoded_dct_coeff / self.scale, axis=0, norm="ortho"))
-        return np.stack(decoded_actions)
-
-    @classmethod
-    def fit(
-        cls,
-        action_data: list[np.array],
-        scale: float = 10,
-        vocab_size: int = 1024,
-        *,
-        time_horizon: int | None = None,
-        action_dim: int | None = None,
-    ) -> "UniversalActionProcessor":
-        # Run DCT over all inputs
-        dct_tokens = [dct(a, axis=0, norm="ortho").flatten() for a in action_data]
-
-        # Quantize and find min token
-        max_token = int(np.around(np.concatenate(dct_tokens) * scale).max())
-        min_token = int(np.around(np.concatenate(dct_tokens) * scale).min())
-        min_vocab_size = max_token - min_token
-
-        assert (
-            min_vocab_size <= vocab_size
-        ), f"Vocab size {vocab_size} is too small for the range of tokens {min_vocab_size}"
-        if min_vocab_size + 100 > vocab_size:
-            logging.warning(
-                f"Initial alphabet size {min_vocab_size} is almost as large as the vocab"
-                f"size {vocab_size}, consider increasing vocab size"
-            )
-
-        # Make token iterator for BPE training
-        def _token_iter():
-            for tokens in dct_tokens:
-                rounded_tokens = np.around(tokens * scale) - min_token
-                rounded_tokens = rounded_tokens.astype(int)
-                string = "".join(map(chr, rounded_tokens))
-                yield string
-
-        # Train BPE tokenizer
-        bpe = ByteLevelBPETokenizer()
-
-        # Set up the entire range of possible tokens as the initial alphabet
-        alphabet = [chr(i) for i in range(max_token - min_token + 1)]
-        trainer = BpeTrainer(
-            vocab_size=vocab_size,
-            min_frequency=2,
-            show_progress=True,
-            special_tokens=[],
-            initial_alphabet=alphabet,
-            max_token_length=10000,
-        )
-
-        # Train the inner tokenizer (don't use ByteLevelBPETokenizer.train_from_iterator()
-        # because it doesn't support custom alphabets)
-        bpe._tokenizer.train_from_iterator(_token_iter(), trainer=trainer)
-
-        return cls(
-            PreTrainedTokenizerFast(tokenizer_object=bpe, clean_up_tokenization_spaces=False),
-            scale=scale,
-            vocab_size=vocab_size,
-            min_token=min_token,
-            time_horizon=time_horizon,
-            action_dim=action_dim,
-        )

fast_tokenizer_local/processor_config.json

@@ -1,11 +0,0 @@
-{
-  "action_dim": 7,
-  "auto_map": {
-    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
-  },
-  "min_token": -32,
-  "processor_class": "UniversalActionProcessor",
-  "scale": 10.0,
-  "time_horizon": 10,
-  "vocab_size": 1024
-}

fast_tokenizer_local/special_tokens_map.json

@@ -1 +0,0 @@
-{}

fast_tokenizer_local/tokenizer_config.json

@@ -1,11 +0,0 @@
-{
-  "added_tokens_decoder": {},
-  "auto_map": {
-    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
-  },
-  "clean_up_tokenization_spaces": false,
-  "extra_special_tokens": {},
-  "model_max_length": 1000000000000000019884624838656,
-  "processor_class": "UniversalActionProcessor",
-  "tokenizer_class": "PreTrainedTokenizerFast"
-}

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
@@ -58,7 +57,6 @@ from lerobot.datasets.utils import (
     load_nested_dataset,
     load_stats,
     load_tasks,
-    load_tasks_high_level,
     update_chunk_file_indices,
     validate_episode_buffer,
     validate_frame,
@@ -162,7 +160,6 @@ class LeRobotDatasetMetadata:
         self.info = load_info(self.root)
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
         self.tasks = load_tasks(self.root)
-        # self.tasks_high_level = load_tasks_high_level(self.root)
         self.episodes = load_episodes(self.root)
         self.stats = load_stats(self.root)
 
@@ -542,15 +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,
@@ -1052,12 +1040,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         # Add task as a string
         task_idx = item["task_index"].item()
         item["task"] = self.meta.tasks.iloc[task_idx].name
-        # Optionally add high level task index
-        if "task_index_high_level" in self.features:
-            high_level_task_idx = item["task_index_high_level"].item()
-            item["robot_utterance"] = self.meta.tasks_high_level.iloc[high_level_task_idx]["robot_utterance"]
-            item["user_prompt"] = self.meta.tasks_high_level.iloc[high_level_task_idx]["user_prompt"]
-
         return item
 
     def __repr__(self):
@@ -1089,7 +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
@@ -1099,15 +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:
         """
@@ -1145,19 +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.
 
@@ -1169,8 +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
 
@@ -1207,40 +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)
@@ -1405,18 +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)
 
@@ -1534,7 +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"
@@ -60,7 +60,6 @@ VIDEO_DIR = "videos"
 
 CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}"
 DEFAULT_TASKS_PATH = "meta/tasks.parquet"
-DEFAULT_TASKS_HIGH_LEVEL_PATH = "meta/tasks_high_level.parquet"
 DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_DATA_PATH = DATA_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_VIDEO_PATH = VIDEO_DIR + "/{video_key}/" + CHUNK_FILE_PATTERN + ".mp4"
@@ -353,9 +352,6 @@ def load_tasks(local_dir: Path) -> pandas.DataFrame:
     tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
     return tasks
 
-def load_tasks_high_level(local_dir: Path) -> pandas.DataFrame:
-    tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_HIGH_LEVEL_PATH)
-    return tasks
 
 def write_episodes(episodes: Dataset, local_dir: Path) -> None:
     """Write episode metadata to a parquet file in the LeRobot v3.0 format.

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/pi05/README_TOKENIZER.md

@@ -1,196 +0,0 @@
-# FAST Tokenizer Training for LeRobotDataset
-
-This directory contains tools for training a FAST (Factorized Action Sequence Tokenizer) on LeRobot datasets.
-
-## Files
-
-- **`train_fast_tokenizer.py`**: Main training script (refactored for LeRobotDataset)
-- **`train_fast_tokenizer_example.md`**: Usage examples and parameter documentation
-- **`MIGRATION_NOTES.md`**: Migration guide from B1K to LeRobotDataset
-
-## Quick Start
-
-```bash
-# Basic usage
-python train_fast_tokenizer.py \
-    --repo_id "lerobot/aloha_sim_insertion_human" \
-    --action_horizon 10 \
-    --encoded_dims "0:14"
-
-# With delta transform
-python train_fast_tokenizer.py \
-    --repo_id "lerobot/aloha_sim_insertion_human" \
-    --action_horizon 10 \
-    --encoded_dims "0:14" \
-    --delta_dims "0,1,2,3,4,5,6,7,8,9,10,11,12,13" \
-    --state_key "observation.state" \
-    --vocab_size 1024
-```
-
-## What is FAST?
-
-FAST is a tokenizer for robotic action sequences that:
-1. Applies DCT (Discrete Cosine Transform) to action chunks
-2. Quantizes DCT coefficients 
-3. Uses BPE (Byte-Pair Encoding) to compress the quantized sequence
-4. Achieves high compression ratios (e.g., 10-20x) while maintaining accuracy
-
-This enables efficient storage and processing of long action sequences in vision-language-action models.
-
-## Requirements
-
-- Python 3.10+
-- LeRobot dataset (either local or from HuggingFace Hub)
-- transformers (for AutoProcessor)
-- numpy
-- torch
-- tyro
-
-## Workflow
-
-```
-LeRobotDataset → Extract Episodes → Apply Delta Transform 
-    ↓
-Select Dimensions → Normalize (q01, q99) → Create Chunks
-    ↓
-Train FAST Tokenizer → Compute Stats → Save
-```
-
-## Parameters Guide
-
-### Essential Parameters
-
-- **`repo_id`**: HuggingFace dataset repository ID
-  - Example: `"lerobot/aloha_sim_insertion_human"`
-  
-- **`action_horizon`**: Length of action sequences to tokenize
-  - Typical: 10-16 steps
-  
-- **`encoded_dims`**: Which action dimensions to encode
-  - Format: `"start:end,start:end"`
-  - Example: `"0:7"` = dimensions 0-6
-  - Example: `"0:3,7:10"` = dimensions 0-2 and 7-9
-
-### Optional Parameters
-
-- **`delta_dims`**: Apply delta transform (action - state) to these dimensions
-  - Format: `"0,1,2,3,4,5"`
-  - Use for position-based actions
-  
-- **`state_key`**: Dataset key containing state observations
-  - Default: `"observation.state"`
-  
-- **`vocab_size`**: BPE vocabulary size
-  - Default: 1024
-  - Larger = better compression but more memory
-  
-- **`scale`**: DCT quantization scale
-  - Default: 10.0
-  - Smaller = finer quantization, larger = coarser
-
-- **`sample_fraction`**: Fraction of action chunks to use per episode
-  - Default: 0.1 (10%)
-  - Increase for small datasets, decrease for large datasets
-
-## Output
-
-The script creates a directory (default: `./fast_tokenizer_{repo_id}`) containing:
-
-1. **Tokenizer files**: Can be loaded with `AutoProcessor.from_pretrained()`
-2. **`metadata.json`**: Contains:
-   - Training configuration
-   - Compression statistics
-   - Dataset information
-
-## Example Output
-
-```
-Loading dataset: lerobot/aloha_sim_insertion_human
-Dataset loaded: 50 episodes, 5000 frames
-Encoding 14 dimensions: 0:14
-Delta dimensions: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
-Action horizon: 10
-Processing 50 episodes...
-Collected 4500 action chunks
-Extracted 14 encoded dimensions
-
-Before normalization - overall stats:
-  Min: -2.3451, Max: 3.1234, Mean: 0.0234, Std: 0.8765
-
-Applied quantile normalization [q01, q99] → [-1, 1]
-
-After normalization - overall stats:
-  Min: -1.0000, Max: 1.0000, Mean: 0.0156, Std: 0.4321
-
-Training FAST tokenizer on 4500 action chunks...
-Action chunk shape: (4500, 10, 14)
-Vocab size: 1024
-DCT scale: 10.0
-✓ Tokenizer training complete!
-
-Compression Statistics:
-  Average compression ratio: 14.23x
-  Mean token length: 9.8
-  P99 token length: 15
-  Min token length: 6
-  Max token length: 18
-
-✅ Saved FAST tokenizer to ./fast_tokenizer_lerobot_aloha_sim_insertion_human
-```
-
-## Using the Trained Tokenizer
-
-```python
-from transformers import AutoProcessor
-
-# Load tokenizer
-tokenizer = AutoProcessor.from_pretrained(
-    "./fast_tokenizer_lerobot_aloha_sim_insertion_human",
-    trust_remote_code=True
-)
-
-# Encode action chunk [horizon, action_dim]
-action_chunk = np.random.randn(10, 14)  # Example
-tokens = tokenizer(action_chunk[None])[0]  # Returns token IDs
-
-# Decode tokens back to actions
-reconstructed = tokenizer.decode(tokens)
-```
-
-## Tips
-
-1. **Start Small**: Use `--max_episodes 10` for initial testing
-2. **Check Dimensions**: Verify encoded dimensions match your robot's action space
-3. **Delta Transform**: Use for position-based actions, not velocity-based
-4. **Normalization**: Ensure dataset has proper statistics computed
-5. **Compression Ratio**: Aim for 10-20x for good balance of compression and accuracy
-
-## Troubleshooting
-
-**Issue**: "No normalization stats found"
-- **Solution**: Compute dataset statistics first, or use raw actions
-
-**Issue**: "Episode too short for action horizon"
-- **Solution**: Reduce `--action_horizon` or filter short episodes
-
-**Issue**: "State key not found"
-- **Solution**: Check dataset features and use correct `--state_key`
-
-**Issue**: Memory error with large datasets
-- **Solution**: Reduce `--sample_fraction` or `--max_episodes`
-
-## Citation
-
-If you use FAST in your research, please cite:
-
-```bibtex
-@article{black2023fast,
-  title={FAST: Factorized Action Sequence Tokenizer for Vision-Language-Action Models},
-  author={Black, Kevin and others},
-  journal={arXiv preprint},
-  year={2023}
-}
-```
-
-
-

src/lerobot/policies/pi05/configuration_pi05.py

@@ -37,11 +37,6 @@ class PI05Config(PreTrainedConfig):
     # Shorter state and action vectors will be padded to these dimensions
     max_state_dim: int = 32
     max_action_dim: int = 32
-    max_action_tokens: int = 32
-    fast_vocab_size: int = 2048
-    
-    # FAST-only mode: train with only discrete action token prediction (no flow matching, no subtask)
-    fast_only: bool = False
 
     # Flow matching parameters: see openpi `PI0Pytorch`
     num_inference_steps: int = 10
@@ -65,8 +60,8 @@ class PI05Config(PreTrainedConfig):
     normalization_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {
             "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,  # Pi0.5 uses quantiles for state
-            "ACTION": NormalizationMode.MEAN_STD,  # Pi0.5 uses quantiles for action
+            "STATE": NormalizationMode.QUANTILES,  # Pi0.5 uses quantiles for state
+            "ACTION": NormalizationMode.QUANTILES,  # Pi0.5 uses quantiles for action
         }
     )
 

src/lerobot/policies/pi05/finetune_pi0.sh

@@ -1,21 +0,0 @@
-lerobot-train \
-    --dataset.repo_id=lerobot \
-    --dataset.root=/fsx/jade_choghari/outputs/collect-data-pgen \
-    --output_dir=/fsx/jade_choghari/outputs/pi0test1 \
-    --job_name=pi0_training \
-    --policy.repo_id=jade_choghari/pi0-base \
-    --policy.path=/fsx/jade_choghari/outputs/pi0_fast_fruit1/checkpoints/last/pretrained_model \
-    --policy.dtype=bfloat16 \
-    --steps=3000 \
-    --save_freq=1000 \
-    --rename_map='{
-        "observation.images.base": "observation.images.base_0_rgb",
-        "observation.images.left_wrist": "observation.images.left_wrist_0_rgb",
-        "observation.images.right_wrist": "observation.images.right_wrist_0_rgb",
-        }' \
-    --batch_size=4 \
-    --policy.device=cuda \
-    # --wandb.enable=true \
-    # --wandb.disable_artifact=true \
-    # --wandb.project=pi05hi-training \
-

src/lerobot/policies/pi05/processor_pi05.py

@@ -33,7 +33,6 @@ from lerobot.processor import (
     ProcessorStep,
     ProcessorStepRegistry,
     RenameObservationsProcessorStep,
-    ActionTokenizerProcessorStep,
     TokenizerProcessorStep,
     UnnormalizerProcessorStep,
 )
@@ -48,15 +47,13 @@ from lerobot.utils.constants import (
 
 @ProcessorStepRegistry.register(name="pi05_prepare_state_tokenizer_processor_step")
 @dataclass
-class Pi05PrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
+class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
     """
     Processor step to prepare the state and tokenize the language input.
     """
 
     max_state_dim: int = 32
     task_key: str = "task"
-    high_level_task_key: str = "user_prompt"
-    subtask_only_key: str = "subtask"
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         transition = transition.copy()
@@ -67,8 +64,6 @@ class Pi05PrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
         tasks = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get(self.task_key)
         if tasks is None:
             raise ValueError("No task found in complementary data")
-        
-        high_level_tasks = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get(self.high_level_task_key)
 
         # TODO: check if this necessary
         state = deepcopy(state)
@@ -81,42 +76,16 @@ class Pi05PrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
         state_np = state.cpu().numpy()
         discretized_states = np.digitize(state_np, bins=np.linspace(-1, 1, 256 + 1)[:-1]) - 1
 
-        # Clean high level tasks first (if available)
-        cleaned_high_level_tasks = []
-        if high_level_tasks is not None:
-            for high_level_task in high_level_tasks:
-                cleaned_high_level_tasks.append(high_level_task.strip().replace("_", " ").replace("\n", " "))
-        
-        # Process low level tasks with state information
-        low_level_prompts = []
-        subtask_only_prompts = []  # Store only the subtask text for prediction
+        full_prompts = []
         for i, task in enumerate(tasks):
             cleaned_text = task.strip().replace("_", " ").replace("\n", " ")
             state_str = " ".join(map(str, discretized_states[i]))
-            
-            # Store only the subtask text (used as prediction target)
-            subtask_only_prompts.append(cleaned_text)
-            
-            if cleaned_high_level_tasks:
-                cleaned_high_level_task = cleaned_high_level_tasks[i]
-                full_prompt = f"High level task: {cleaned_high_level_task}; State: {state_str}; Subtask: {cleaned_text}"
-            else:
-                full_prompt = f"Task: {cleaned_text}, State: {state_str};\n" #remove Action by jade
+            full_prompt = f"Task: {cleaned_text}, State: {state_str};\nAction: "
+            full_prompts.append(full_prompt)
 
-            low_level_prompts.append(full_prompt)
-
-        transition[TransitionKey.COMPLEMENTARY_DATA][self.task_key] = low_level_prompts
-        transition[TransitionKey.COMPLEMENTARY_DATA][self.subtask_only_key] = subtask_only_prompts
-        
-        # Process high level tasks without state information (if available)
-        if high_level_tasks is not None:
-            high_level_prompts = []
-            for i, cleaned_high_level_task in enumerate(cleaned_high_level_tasks):
-                state_str = " ".join(map(str, discretized_states[i]))
-                full_prompt = f"High level task: {cleaned_high_level_task}; State: {state_str}; Subtask:"
-                high_level_prompts.append(full_prompt)
-            
-            transition[TransitionKey.COMPLEMENTARY_DATA][self.high_level_task_key] = high_level_prompts
+        transition[TransitionKey.COMPLEMENTARY_DATA][self.task_key] = full_prompts
+        # Normalize state to [-1, 1] range if needed (assuming it's already normalized by normalizer processor step!!)
+        # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
         return transition
 
     def transform_features(
@@ -159,27 +128,25 @@ def make_pi05_pre_post_processors(
     Returns:
         A tuple containing the configured pre-processor and post-processor pipelines.
     """
+
     # Add remaining processors
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),  # To mimic the same processor as pretrained one
         AddBatchDimensionProcessorStep(),
-        # NOTE: NormalizerProcessorStep MUST come before Pi05PrepareStateAndLanguageTokenizerProcessorStep
+        # NOTE: NormalizerProcessorStep MUST come before Pi05PrepareStateTokenizerProcessorStep
         # because the tokenizer step expects normalized state in [-1, 1] range for discretization
         NormalizerProcessorStep(
             features={**config.input_features, **config.output_features},
             norm_map=config.normalization_mapping,
             stats=dataset_stats,
         ),
-        Pi05PrepareStateAndLanguageTokenizerProcessorStep(max_state_dim=config.max_state_dim),
+        Pi05PrepareStateTokenizerProcessorStep(max_state_dim=config.max_state_dim),
         TokenizerProcessorStep(
             tokenizer_name="google/paligemma-3b-pt-224",
             max_length=config.tokenizer_max_length,
             padding_side="right",
             padding="max_length",
         ),
-        ActionTokenizerProcessorStep(
-            tokenizer_name="/fsx/jade_choghari/outputs/fast_tokenizer", # TODO: jade put the PI
-        ),
         DeviceProcessorStep(device=config.device),
     ]
 
@@ -189,7 +156,7 @@ def make_pi05_pre_post_processors(
         ),
         DeviceProcessorStep(device="cpu"),
     ]
-    
+
     return (
         PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
             steps=input_steps,

src/lerobot/policies/pi05/train.sh

@@ -1,23 +0,0 @@
-export CUDA_LAUNCH_BLOCKING=1 
-lerobot-train \
-    --dataset.repo_id=local \
-    --dataset.root=/fsx/jade_choghari/outputs/collect-data-pgen \
-    --output_dir=/fsx/jade_choghari/outputs/pi0_fast_fruit2 \
-    --job_name=pi0_training \
-    --policy.repo_id=jade_choghari/pi0-base1 \
-    --policy.path=lerobot/pi05_base \
-    --policy.dtype=bfloat16 \
-    --steps=200000 \
-    --save_freq=5000 \
-    --rename_map='{
-        "observation.images.base": "observation.images.base_0_rgb",
-        "observation.images.left_wrist": "observation.images.left_wrist_0_rgb",
-        "observation.images.right_wrist": "observation.images.right_wrist_0_rgb",
-        }' \
-    --batch_size=16 \
-    --policy.device=cuda \
-    --policy.fast_only=true \
-    # --wandb.enable=true \
-    # --wandb.disable_artifact=true \
-    # --wandb.project=pi05hi-training \
-# /fsx/jade_choghari/.cache/huggingface/lerobot/jadechoghari/collect-data