lerobot dataset fix

* add visualize_dataset call from `lerobot_dataset_viz` in web record server

* add delete button

* fixes

* remove viz

* unused import

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

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

.github/workflows/documentation.yml

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

.github/workflows/fast_tests.yml

@@ -45,7 +45,6 @@ permissions:
 env:
   UV_VERSION: "0.8.0"
   PYTHON_VERSION: "3.10"
-  DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
 
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.
 concurrency:
@@ -60,12 +59,19 @@ 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

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

.github/workflows/nightly.yml

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

.github/workflows/release.yml

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

.github/workflows/stale.yml

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

.github/workflows/unbound_deps_tests.yml

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

benchmarks/video/benchmark.py

@@ -1,94 +0,0 @@
-#!/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

@@ -1,102 +0,0 @@
-#!/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

@@ -21,11 +21,13 @@ 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
@@ -35,13 +37,13 @@ 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_torchvision,
+    decode_video_frames,
     encode_video_frames,
 )
 from lerobot.utils.constants import OBS_IMAGE
+from lerobot.utils.utils import TimerManager
 
 BASE_ENCODING = OrderedDict(
     [
@@ -86,7 +88,7 @@ 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")
@@ -97,21 +99,21 @@ 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)
@@ -125,7 +127,7 @@ 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
@@ -149,18 +151,6 @@ 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,
@@ -172,8 +162,8 @@ def benchmark_decoding(
     num_workers: int = 4,
     save_frames: bool = False,
 ) -> dict:
-    def process_sample(sample: int):
-        time_benchmark = TimeBenchmark()
+    def process_sample(sample: int, lock: Lock):
+        time_benchmark = TimerManager(log=False)
         timestamps = sample_timestamps(timestamps_mode, ep_num_images, fps)
         num_frames = len(timestamps)
         result = {
@@ -182,13 +172,13 @@ def benchmark_decoding(
             "mse_values": [],
         }
 
-        with time_benchmark:
+        with time_benchmark, lock:
             frames = decode_video_frames(video_path, timestamps=timestamps, tolerance_s=5e-1, backend=backend)
-        result["load_time_video_ms"] = time_benchmark.result_ms / num_frames
+        result["load_time_video_ms"] = (time_benchmark.last * 1000) / num_frames
 
         with time_benchmark:
             original_frames = load_original_frames(imgs_dir, timestamps, fps)
-        result["load_time_images_ms"] = time_benchmark.result_ms / num_frames
+        result["load_time_images_ms"] = (time_benchmark.last * 1000) / num_frames
 
         frames_np, original_frames_np = frames.numpy(), original_frames.numpy()
         for i in range(num_frames):
@@ -215,8 +205,10 @@ 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) for i in range(num_samples)]
+        futures = [executor.submit(process_sample, i, shared_lock) 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"])
@@ -358,24 +350,27 @@ def main(
                 imgs_dir = output_dir / "images" / dataset.repo_id.replace("/", "_")
                 # We only use the first episode
                 save_first_episode(imgs_dir, dataset)
-                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
+                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():
                         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)
@@ -409,9 +404,9 @@ if __name__ == "__main__":
         nargs="*",
         default=[
             "lerobot/pusht_image",
-            "aliberts/aloha_mobile_shrimp_image",
-            "aliberts/paris_street",
-            "aliberts/kitchen",
+            "lerobot/aloha_mobile_shrimp_image",
+            "lerobot/paris_street",
+            "lerobot/kitchen",
         ],
         help="Datasets repo-ids to test against. First episodes only are used. Must be images.",
     )
@@ -419,7 +414,7 @@ if __name__ == "__main__":
         "--vcodec",
         type=str,
         nargs="*",
-        default=["libx264", "hevc", "libsvtav1"],
+        default=["h264", "hevc", "libsvtav1"],
         help="Video codecs to be tested",
     )
     parser.add_argument(
@@ -468,7 +463,7 @@ if __name__ == "__main__":
         "--backends",
         type=str,
         nargs="*",
-        default=["pyav", "video_reader"],
+        default=["torchcodec", "pyav"],
         help="Torchvision decoding backend to be tested.",
     )
     parser.add_argument(

docs/source/_toctree.yml

@@ -9,6 +9,8 @@
     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
@@ -37,6 +39,8 @@
     title: π₀.₅ (Pi05)
   - local: groot
     title: NVIDIA GR00T N1.5
+  - local: xvla
+    title: X-VLA
   title: "Policies"
 - sections:
   - local: async
@@ -47,8 +51,8 @@
 - sections:
   - local: envhub
     title: Environments from the Hub
-  - local: il_sim
-    title: Imitation Learning in Sim
+  - local: envhub_leisaac
+    title: Control & Train Robots in Sim (LeIsaac)
   - local: libero
     title: Using Libero
   - local: metaworld
@@ -79,11 +83,19 @@
     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="fracapuano/smolvla_async",
+    pretrained_name_or_path="<user>/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

@@ -0,0 +1,175 @@
+# 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

@@ -0,0 +1,206 @@
+# 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

@@ -0,0 +1,301 @@
+# 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

@@ -201,7 +201,8 @@ from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader
 from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
-from lerobot.record import record_loop
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.processor import make_default_processors
 
 NUM_EPISODES = 5
 FPS = 30
@@ -209,12 +210,19 @@ EPISODE_TIME_SEC = 60
 RESET_TIME_SEC = 10
 TASK_DESCRIPTION = "My task description"
 
-# Create the robot and teleoperator configurations
-camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+# Create robot configuration
 robot_config = SO100FollowerConfig(
-    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", cameras=camera_config
+    id="my_awesome_follower_arm",
+    cameras={
+        "front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS) # Optional: fourcc="MJPG" for troubleshooting OpenCV async error.
+    },
+    port="/dev/tty.usbmodem58760434471",
+)
+
+teleop_config = SO100LeaderConfig(
+    id="my_awesome_leader_arm",
+    port="/dev/tty.usbmodem585A0077581",
 )
-teleop_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")
 
 # Initialize the robot and teleoperator
 robot = SO100Follower(robot_config)
@@ -243,6 +251,9 @@ init_rerun(session_name="recording")
 robot.connect()
 teleop.connect()
 
+# Create the required processors
+teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
 episode_idx = 0
 while episode_idx < NUM_EPISODES and not events["stop_recording"]:
     log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
@@ -251,6 +262,9 @@ while episode_idx < NUM_EPISODES and not events["stop_recording"]:
         robot=robot,
         events=events,
         fps=FPS,
+        teleop_action_processor=teleop_action_processor,
+        robot_action_processor=robot_action_processor,
+        robot_observation_processor=robot_observation_processor,
         teleop=teleop,
         dataset=dataset,
         control_time_s=EPISODE_TIME_SEC,
@@ -265,6 +279,9 @@ while episode_idx < NUM_EPISODES and not events["stop_recording"]:
             robot=robot,
             events=events,
             fps=FPS,
+            teleop_action_processor=teleop_action_processor,
+            robot_action_processor=robot_action_processor,
+            robot_observation_processor=robot_observation_processor,
             teleop=teleop,
             control_time_s=RESET_TIME_SEC,
             single_task=TASK_DESCRIPTION,
@@ -393,7 +410,7 @@ 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 busy_wait
+from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
 
 episode_idx = 0
@@ -415,7 +432,7 @@ for idx in range(dataset.num_frames):
     }
     robot.send_action(action)
 
-    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
+    precise_sleep(1.0 / dataset.fps - (time.perf_counter() - t0))
 
 robot.disconnect()
 ```
@@ -428,7 +445,7 @@ 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/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/lerobot_train.py) script. A few arguments are required. Here is an example command:
 
 ```bash
 lerobot-train \
@@ -485,7 +502,7 @@ huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
 
 ## Run inference and evaluate your policy
 
-You can use the `record` script from [`lerobot/record.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:
+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:
 
 <hfoptions id="eval">
 <hfoption id="Command">

docs/source/il_sim.mdx

@@ -1,220 +0,0 @@
-# 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 python-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config
+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
 ```
 
 For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)

docs/source/libero.mdx

@@ -62,6 +62,11 @@ 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/openarms.mdx

@@ -0,0 +1,328 @@
+# OpenArms Robot
+
+OpenArms is a 7 DOF robotic arm with a gripper, designed by [Enactic, Inc.](https://www.enactic.com/) It uses Damiao motors controlled via CAN bus communication and MIT control mode for smooth, precise motion.
+
+## Hardware Overview
+
+- **7 DOF per arm** (14 DOF total for dual arm setup)
+- **1 gripper per arm** (2 grippers total)
+- **Damiao motors** with 4 different types:
+  - **DM8009** (DM-J8009P-2EC) for shoulders (J1, J2) - high torque
+  - **DM4340** for shoulder rotation and elbow (J3, J4)
+  - **DM4310** (DM-J4310-2EC V1.1) for wrist (J5, J6, J7) and gripper (J8)
+- **24V power supply** required
+- **CAN interface device**:
+  - **Linux**: Any SocketCAN-compatible adapter
+  - **macOS**: CANable, PEAK PCAN-USB, or Kvaser USBcan
+- Proper CAN wiring (CANH, CANL, 120Ω termination)
+
+
+## Motor Configuration
+
+Each arm has the following motor configuration based on the [OpenArm setup guide](https://docs.openarm.dev/software/setup/):
+
+| Joint | Motor | Motor Type | Sender CAN ID | Receiver ID | Description |
+|-------|-------|------------|---------------|-------------|-------------|
+| J1 | joint_1 | DM8009 | 0x01 | 0x11 | Shoulder pan |
+| J2 | joint_2 | DM8009 | 0x02 | 0x12 | Shoulder lift |
+| J3 | joint_3 | DM4340 | 0x03 | 0x13 | Shoulder rotation |
+| J4 | joint_4 | DM4340 | 0x04 | 0x14 | Elbow flex |
+| J5 | joint_5 | DM4310 | 0x05 | 0x15 | Wrist roll |
+| J6 | joint_6 | DM4310 | 0x06 | 0x16 | Wrist pitch |
+| J7 | joint_7 | DM4310 | 0x07 | 0x17 | Wrist rotation |
+| J8 | gripper | DM4310 | 0x08 | 0x18 | Gripper |
+
+For dual arm setups, the left arm uses IDs 0x09-0x10 for joints 1-8 with the same motor types.
+
+## Quick Start 
+
+```bash
+# Install system dependencies
+sudo apt install can-utils iproute2
+
+# Install LeRobot with OpenArms support
+pip install -e ".[openarms]"
+```
+
+## Setup Guide
+
+### Step 1: Motor ID Configuration
+
+**IMPORTANT**: Before using the robot, motors must be configured with the correct CAN IDs.
+
+Refer to the [OpenArm Motor ID Configuration Guide](https://docs.openarm.dev/software/setup/motor-id) for detailed instructions using the Damiao Debugging Tools on Windows.
+
+Key points:
+- Each motor needs a unique **Sender CAN ID** (0x01-0x08)
+- Each motor needs a unique **Receiver/Master ID** (0x11-0x18)
+- Use the Damiao Debugging Tools to set these IDs
+
+### Step 2: Setup CAN Interface
+
+Configure your CAN interface as described in the [OpenArm CAN Setup Guide](https://docs.openarm.dev/software/setup/can-setup):
+
+#### Linux (SocketCAN)
+
+```bash
+# Find your CAN interface
+ip link show
+
+# Configure can0, 1, 2, 3
+sudo ip link set can0 down
+sudo ip link set can0 type can bitrate 1000000
+sudo ip link set can0 up
+
+sudo ip link set can1 down
+sudo ip link set can1 type can bitrate 1000000
+sudo ip link set can1 up
+
+sudo ip link set can2 down
+sudo ip link set can2 type can bitrate 1000000
+sudo ip link set can2 up
+
+sudo ip link set can3 down
+sudo ip link set can3 type can bitrate 1000000
+sudo ip link set can3 up
+
+# Verify configuration
+ip link show can0
+```
+
+or run:
+
+`examples/openarms/setup_can.sh`
+
+### Testing canbus and motor connection
+
+Please run this script to check if all motors can be found and to find your can-fd speed: `python examples/openarms/debug_can_communication.py`
+
+## Usage
+
+### Basic Setup
+
+
+```python
+from lerobot.robots.openarms import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+
+# Configure for dual arm setup
+config = OpenArmsFollowerConfig(
+    port="can0",
+    can_interface="socketcan",  # Or "auto" for auto-detection
+    id="openarms_dual",
+    is_dual_arm=True,
+)
+
+robot = OpenArmsFollower(config)
+robot.connect()
+```
+
+### Calibration
+
+On first use, you'll need to calibrate the robot:
+
+```python
+robot.calibrate()
+```
+
+The calibration process will:
+1. Disable torque on all motors
+2. Ask you to position arms in **hanging position with grippers closed**
+3. Set this as the zero position
+4. Ask you to move each joint through its full range
+5. Record min/max positions for each joint
+6. Save calibration to file
+
+### Reading Observations
+
+The robot provides comprehensive state information:
+
+```python
+observation = robot.get_observation()
+
+# Observation includes for each motor:
+# - {motor_name}.pos: Position in degrees
+# - {motor_name}.vel: Velocity in degrees/second  
+# - {motor_name}.torque: Motor torque
+# - {camera_name}: Camera images (if configured)
+
+print(f"Right arm joint 1 position: {observation['right_joint_1.pos']:.1f}°")
+print(f"Right arm joint 1 velocity: {observation['right_joint_1.vel']:.1f}°/s")
+print(f"Right arm joint 1 torque: {observation['right_joint_1.torque']:.3f} N·m")
+```
+
+### Sending Actions
+
+```python
+# Send target positions (in degrees)
+action = {
+    "right_joint_1.pos": 45.0,
+    "right_joint_2.pos": -30.0,
+    # ... all joints
+    "right_gripper.pos": 45.0,  # Half-closed
+}
+
+actual_action = robot.send_action(action)
+```
+
+### Gripper Control
+
+```python
+# Open gripper
+robot.open_gripper(arm="right")
+
+# Close gripper  
+robot.close_gripper(arm="right")
+```
+
+## Safety Features
+
+### 1. Maximum Relative Target
+
+Limits how far a joint can move in a single command to prevent sudden movements:
+
+```python
+config = OpenArmsFollowerConfig(
+    port="can0",
+    # Limit all joints to 10 degrees per command
+    max_relative_target=10.0,
+    
+    # Or set per-motor limits
+    max_relative_target={
+        "right_joint_1": 15.0,  # Slower moving joint
+        "right_joint_2": 10.0,
+        "right_gripper": 5.0,   # Very slow gripper
+    }
+)
+```
+
+**How it works**: If current position is 50° and you command 80°, with `max_relative_target=10.0`, the robot will only move to 60° in that step.
+
+### 2. Torque Limits
+
+Control maximum torque output, especially important for grippers and teleoperation:
+
+```python
+config = OpenArmsFollowerConfig(
+    port="can0",
+    # Gripper torque limit (fraction of motor's max torque)
+    gripper_torque_limit=0.5,  # 50% of max torque
+)
+```
+
+Lower torque limits prevent damage when gripping delicate objects.
+
+### 3. MIT Control Gains
+
+Control responsiveness and stability via PID-like gains:
+
+```python
+config = OpenArmsFollowerConfig(
+    port="can0",
+    position_kp=10.0,  # Position gain (higher = more responsive)
+    position_kd=0.5,   # Velocity damping (higher = more damped)
+)
+```
+
+**Guidelines**:
+- **For following (robot)**: Higher gains for responsiveness
+  - `position_kp=10.0`, `position_kd=0.5`
+- **For teleoperation (leader)**: Lower gains or disable torque for manual movement
+  - `manual_control=True` (torque disabled)
+
+### 4. Velocity Limits
+
+Velocity limits are enforced by the Damiao motors based on motor type. For DM4310:
+- Max velocity: 30 rad/s ≈ 1718°/s
+
+The motors will automatically limit velocity to safe values.
+
+## Teleoperation
+
+### Leader Arm Setup
+
+The leader arm is moved manually (torque disabled) to generate commands:
+
+```python
+from lerobot.teleoperators.openarms import OpenArmsLeader
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+
+config = OpenArmsLeaderConfig(
+    port="can1",  # Separate CAN interface for leader
+    id="openarms_leader",
+    manual_control=True,  # Torque disabled for manual movement
+    is_dual_arm=True,
+)
+
+leader = OpenArmsLeader(config)
+leader.connect()
+
+# Read current position as action
+action = leader.get_action()
+# action contains positions for all joints in degrees
+```
+
+### Safety Considerations for Teleoperation
+
+1. **Use separate CAN interfaces** for leader and follower to avoid conflicts
+2. **Enable max_relative_target** on follower to smooth abrupt movements
+3. **Lower torque limits** on follower to prevent damage from tracking errors
+4. **Test with one arm** before enabling dual arm teleoperation
+5. **Have emergency stop** ready (power switch or CAN disable)
+
+```python
+# Recommended follower config for teleoperation
+follower_config = OpenArmsFollowerConfig(
+    port="can0",
+    max_relative_target=5.0,  # Small steps for smooth following
+    gripper_torque_limit=0.3, # Low torque for safety
+    position_kp=5.0,  # Lower gains for gentler following
+    position_kd=0.3,
+)
+```
+
+## Troubleshooting
+
+### Motor Shaking/Unstable
+
+- **Lower control gains**: Reduce `position_kp` and `position_kd`
+- **Check calibration**: Re-run calibration procedure
+- **Verify power**: Insufficient current can cause instability
+- **Check mechanical**: Loose connections, binding, or damaged components
+
+### CAN Bus Errors
+
+```bash
+# Check for errors
+ip -s link show can0
+
+# Reset CAN interface
+sudo ip link set can0 down
+sudo ip link set can0 up
+```
+
+### Control Mode
+
+OpenArms uses **MIT control mode** which allows simultaneous control of:
+- Position (degrees)
+- Velocity (degrees/second)
+- Torque (N·m)
+- Position gain (Kp)
+- Velocity damping (Kd)
+
+### Communication
+
+- **Protocol**: CAN 2.0 at 1 Mbps (or CAN-FD at 5 Mbps)
+- **Frame format**: Standard 11-bit IDs
+- **Update rate**: Typically 50-100 Hz depending on motor count
+- **Latency**: ~10-20ms per motor command
+
+## References
+
+- [OpenArm Official Documentation](https://docs.openarm.dev/)
+- [OpenArm Setup Guide](https://docs.openarm.dev/software/setup/)
+- [Motor ID Configuration](https://docs.openarm.dev/software/setup/motor-id)
+- [CAN Interface Setup](https://docs.openarm.dev/software/setup/can-setup)
+- [Motor Communication Test](https://docs.openarm.dev/software/setup/configure-test)
+- [Damiao Motor Documentation](https://wiki.seeedstudio.com/damiao_series/)
+- [Enactic GitHub](https://github.com/enactic/openarm_can)

docs/source/so101.mdx

@@ -30,131 +30,6 @@ 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
@@ -340,6 +215,131 @@ 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

@@ -0,0 +1,42 @@
+# 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

@@ -0,0 +1,208 @@
+# 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. 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
+- **MuJoCo simulation mode** for testing policies without the physical robot
+
+---
+
+## 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.
+
+---
+
+## Extra: Running in Simulation Mode (MuJoCo)
+
+You can now test and develop policies without a physical robot using MuJoCo. to do so set `is_simulation=True` in config.
+
+## Additional Resources
+
+- [Unitree SDK Documentation](https://github.com/unitreerobotics/unitree_sdk2_python)
+- [GR00T Policy Repository](https://huggingface.co/nepyope/GR00T-WholeBodyControl_g1)
+- [LeRobot Documentation](https://github.com/huggingface/lerobot)
+- [Unitree_IL_Lerobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
+
+---
+
+_Last updated: December 2025_

docs/source/using_dataset_tools.mdx

@@ -11,13 +11,14 @@ LeRobot provides several utilities for manipulating datasets:
 3. **Merge Datasets** - Combine multiple datasets into one. The datasets must have identical features, and episodes are concatenated in the order specified in `repo_ids`
 4. **Add Features** - Add new features to a dataset
 5. **Remove Features** - Remove features from a dataset
+6. **Convert to Video** - Convert image-based datasets to video format for efficient storage
 
 The core implementation is in `lerobot.datasets.dataset_tools`.
 An example script detailing how to use the tools API is available in `examples/dataset/use_dataset_tools.py`.
 
 ## Command-Line Tool: lerobot-edit-dataset
 
-`lerobot-edit-dataset` is a command-line script for editing datasets. It can be used to delete episodes, split datasets, merge datasets, add features, and remove features.
+`lerobot-edit-dataset` is a command-line script for editing datasets. It can be used to delete episodes, split datasets, merge datasets, add features, remove features, and convert image datasets to video format.
 
 Run `lerobot-edit-dataset --help` for more information on the configuration of each operation.
 
@@ -86,9 +87,71 @@ lerobot-edit-dataset \
     --operation.feature_names "['observation.images.top']"
 ```
 
+#### Convert to Video
+
+Convert an image-based dataset to video format, creating a new LeRobotDataset where images are stored as videos. This is useful for reducing storage requirements and improving data loading performance. The new dataset will have the exact same structure as the original, but with images encoded as MP4 videos in the proper LeRobot format.
+
+```bash
+# Local-only: Save to a custom output directory (no hub push)
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_to_video \
+    --operation.output_dir /path/to/output/pusht_video
+
+# Save with new repo_id (local storage)
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --new_repo_id lerobot/pusht_video \
+    --operation.type convert_to_video
+
+# Convert and push to Hugging Face Hub
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --new_repo_id lerobot/pusht_video \
+    --operation.type convert_to_video \
+    --push_to_hub true
+
+# Convert with custom video codec and quality settings
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_to_video \
+    --operation.output_dir outputs/pusht_video \
+    --operation.vcodec libsvtav1 \
+    --operation.pix_fmt yuv420p \
+    --operation.g 2 \
+    --operation.crf 30
+
+# Convert only specific episodes
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_to_video \
+    --operation.output_dir outputs/pusht_video \
+    --operation.episode_indices "[0, 1, 2, 5, 10]"
+
+# Convert with multiple workers for parallel processing
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_to_video \
+    --operation.output_dir outputs/pusht_video \
+    --operation.num_workers 8
+```
+
+**Parameters:**
+
+- `output_dir`: Custom output directory (optional - by default uses `new_repo_id` or `{repo_id}_video`)
+- `vcodec`: Video codec to use - options: `h264`, `hevc`, `libsvtav1` (default: `libsvtav1`)
+- `pix_fmt`: Pixel format - options: `yuv420p`, `yuv444p` (default: `yuv420p`)
+- `g`: Group of pictures (GOP) size - lower values give better quality but larger files (default: 2)
+- `crf`: Constant rate factor - lower values give better quality but larger files, 0 is lossless (default: 30)
+- `fast_decode`: Fast decode tuning option (default: 0)
+- `episode_indices`: List of specific episodes to convert (default: all episodes)
+- `num_workers`: Number of parallel workers for processing (default: 4)
+
+**Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). All episodes, stats, and tasks are preserved.
+
 ### Push to Hub
 
-Add the `--push_to_hub` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:
+Add the `--push_to_hub true` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:
 
 ```bash
 lerobot-edit-dataset \
@@ -96,7 +159,45 @@ lerobot-edit-dataset \
     --new_repo_id lerobot/pusht_after_deletion \
     --operation.type delete_episodes \
     --operation.episode_indices "[0, 2, 5]" \
-    --push_to_hub
+    --push_to_hub true
 ```
 
 There is also a tool for adding features to a dataset that is not yet covered in `lerobot-edit-dataset`.
+
+# Dataset Visualization
+
+## Online Visualization
+
+When you record a dataset using `lerobot`, it automatically uploads to the Hugging Face Hub unless you specify otherwise. To view the dataset online, use our **LeRobot Dataset Visualizer**, available at:
+https://huggingface.co/spaces/lerobot/visualize_dataset
+
+## Local Visualization
+
+You can also visualize episodes from a dataset locally using our command-line tool.
+
+**From the Hugging Face Hub:**
+
+```bash
+lerobot-dataset-viz \
+    --repo-id lerobot/pusht \
+    --episode-index 0
+```
+
+**From a local folder:**
+Add the `--root` option and set `--mode local`. For example, to search in `./my_local_data_dir/lerobot/pusht`:
+
+```bash
+lerobot-dataset-viz \
+    --repo-id lerobot/pusht \
+    --root ./my_local_data_dir \
+    --mode local \
+    --episode-index 0
+```
+
+Once executed, the tool opens `rerun.io` and displays the camera streams, robot states, and actions for the selected episode.
+
+For advanced usage—including visualizing datasets stored on a remote server—run:
+
+```bash
+lerobot-dataset-viz --help
+```

docs/source/xvla.mdx

@@ -0,0 +1,528 @@
+# X-VLA: The First Soft-Prompted Robot Foundation Model for Any Robot, Any Task
+
+## Overview
+
+For years, robotics has aspired to build agents that can follow natural human instructions and operate dexterously across many environments and robot bodies. Recent breakthroughs in LLMs and VLMs suggest a path forward: extend these foundation-model architectures to embodied control by grounding them in actions. This has led to the rise of Vision-Language-Action (VLA) models, with the hope that a single generalist model could combine broad semantic understanding with robust manipulation skills.
+
+But training such models is difficult. Robot data is fragmented across platforms, sensors, embodiments, and collection protocols. Heterogeneity appears everywhere: different arm configurations, different action spaces, different camera setups, different visual domains, and different task distributions. These inconsistencies create major distribution shifts that make pretraining unstable and adaptation unreliable.
+
+Inspired by meta-learning and prompt learning, we ask: **"What if a VLA model could learn the structure of each robot and dataset the same way LLMs learn tasks, through prompts?"**
+
+**X-VLA** is a soft-prompted, flow-matching VLA framework that treats each hardware setup as a "task" and encodes it using a small set of learnable embeddings. These **Soft Prompts** capture embodiment and domain-specific variations, guiding the Transformer from the earliest stages of multimodal fusion. With this mechanism, X-VLA can reconcile diverse robot morphologies, data types, and sensor setups within a single unified architecture.
+
+<p align="center">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-architecture.png"
+    alt="XVLA Architecture"
+    style="max-width: 100%; height: auto; width: 800px;"
+  />
+</p>
+
+Built from pure Transformer encoders, X-VLA scales naturally with model size and dataset diversity. Across 6 simulation benchmarks and 3 real robots, Soft Prompts consistently outperform existing methods in handling hardware and domain differences. X-VLA-0.9B, trained on 290K episodes spanning seven robotic platforms, learns an embodiment-agnostic generalist policy in Phase I, and adapts efficiently to new robots in Phase II simply by learning a new set of prompts, while keeping the backbone frozen.
+
+<p align="center">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-architecture2.png"
+    alt="XVLA Architecture 2"
+    style="width: 60%; height: auto;"
+  />
+</p>
+
+With only 1% of parameters tuned (9M), X-VLA-0.9B achieves near-π₀ performance on LIBERO and Simpler-WidowX, despite using **300× fewer trainable parameters**. It also demonstrates strong real-world dexterity with minimal demonstrations, including folding cloths in under two minutes.
+
+<p align="center">
+  <img
+    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-fold.png"
+    alt="XVLA fold visualization"
+    style="width: 95%; max-width: 1100px; height: auto;"
+  />
+</p>
+
+X-VLA shows that generalist robot intelligence does not require increasingly complex architectures, only the right way to absorb heterogeneity. Soft Prompts offer a simple, scalable mechanism for unifying diverse robotic data, paving the way toward adaptable, cross-embodiment robot foundation models.
+
+## Installation
+
+After installing LeRobot, install the X-VLA dependencies:
+
+```bash
+pip install -e .[xvla]
+```
+
+After the new release, you'll be able to do:
+
+```bash
+pip install lerobot[xvla]
+```
+
+## Quick Start
+
+### Basic Usage
+
+To use X-VLA in your LeRobot configuration, specify the policy type as:
+
+```bash
+policy.type=xvla
+```
+
+### Evaluating Pre-trained Checkpoints
+
+Example evaluation with LIBERO:
+
+```bash
+lerobot-eval \
+  --policy.path="lerobot/xvla-libero" \
+  --env.type=libero \
+  --env.task=libero_spatial,libero_goal,libero_10 \
+  --env.control_mode=absolute \
+  --eval.batch_size=1 \
+  --eval.n_episodes=1 \
+  --env.episode_length=800 \
+  --seed=142
+```
+
+## Available Checkpoints
+
+### 🎯 Base Model
+
+**[lerobot/xvla-base](https://huggingface.co/lerobot/xvla-base)**
+
+A 0.9B parameter instantiation of X-VLA, trained with a carefully designed data processing and learning recipe. The training pipeline consists of two phases:
+
+- **Phase I: Pretraining** - Pretrained on 290K episodes from Droid, Robomind, and Agibot, spanning seven platforms across five types of robotic arms (single-arm to bi-manual setups). By leveraging soft prompts to absorb embodiment-specific variations, the model learns an embodiment-agnostic generalist policy.
+
+- **Phase II: Domain Adaptation** - Adapted to deployable policies for target domains. A new set of soft prompts is introduced and optimized to encode the hardware configuration of the novel domain, while the pretrained backbone remains frozen.
+
+### Simulation Checkpoints
+
+**[lerobot/xvla-libero](https://huggingface.co/lerobot/xvla-libero)**
+
+Achieves 93% success rate on LIBERO benchmarks. Fine-tuned from the base model for simulation tasks.
+
+**[lerobot/xvla-widowx](https://huggingface.co/lerobot/xvla-widowx)**
+
+Fine-tuned on BridgeData for pick-and-place experiments on compact WidowX platforms. Demonstrates robust manipulation capabilities.
+
+### 🤖 Real-World Checkpoints
+
+**[lerobot/xvla-folding](https://huggingface.co/lerobot/xvla-folding)**
+
+A fine-tuned dexterous manipulation model trained on the high-quality Soft-FOLD cloth folding dataset. Achieves 100% success rate over 2 hours of continuous cloth folding.
+
+**[lerobot/xvla-agibot-world](https://huggingface.co/lerobot/xvla-agibot-world)**
+
+Optimized for AgileX robot dexterous manipulation tasks.
+
+**[lerobot/xvla-google-robot](https://huggingface.co/lerobot/xvla-google-robot)**
+
+Adapted for Google Robot platforms.
+
+## Training X-VLA
+
+### Recommended Training Configuration
+
+When fine-tuning X-VLA for a new embodiment or task, we recommend not freezing the VLM, and also setting the `policy.dtype=bfloat16` to not hit OOM errors.
+
+```bash
+lerobot-train \
+  --dataset.repo_id=YOUR_DATASET \
+  --output_dir=./outputs/xvla_training \
+  --job_name=xvla_training \
+  --policy.path="lerobot/xvla-base" \
+  --policy.repo_id="HF_USER/xvla-your-robot" \
+  --policy.dtype=bfloat16 \
+  --policy.action_mode=auto \
+  --steps=20000 \
+  --policy.device=cuda \
+  --policy.freeze_vision_encoder=false \
+  --policy.freeze_language_encoder=false \
+  --policy.train_policy_transformer=true \
+  --policy.train_soft_prompts=true \
+```
+
+### Training Parameters Explained
+
+| Parameter                  | Default | Description                                    |
+| -------------------------- | ------- | ---------------------------------------------- |
+| `freeze_vision_encoder`    | `false` | Do not freeze the VLM vision encoder weights   |
+| `freeze_language_encoder`  | `false` | Do not freeze the VLM language encoder weights |
+| `train_policy_transformer` | `true`  | Allow policy transformer layers to train       |
+| `train_soft_prompts`       | `true`  | Allow soft prompts to train                    |
+
+**💡 Best Practice**: For Phase II adaptation to new embodiments, do not freeze the VLM encoders and also train the policy transformer and soft prompts.
+
+### Example: Training on Bimanual Robot
+
+```bash
+lerobot-train \
+  --dataset.repo_id=pepijn223/bimanual-so100-handover-cube \
+  --output_dir=./outputs/xvla_bimanual \
+  --job_name=xvla_so101_training \
+  --policy.path="lerobot/xvla-base" \
+  --policy.dtype=bfloat16 \
+  --policy.repo_id="YOUR_USERNAME/xvla-biso101" \
+  --steps=3000 \
+  --policy.device=cuda \
+  --policy.action_mode=so101_bimanual \
+  --policy.freeze_vision_encoder=false \
+  --policy.freeze_language_encoder=false \
+  --policy.train_policy_transformer=true \
+  --policy.train_soft_prompts=true
+```
+
+💡 **Best Performance:** If you have sufficient computational resources and want to achieve best X-VLA finetuning performance, you should follow the official finetuning strategy:
+
+**🔥 Full-finetune all components with a custom learning-rate scheme**
+
+To ensure stable optimization, the Vision-Language Model (VLM) must be trained with only 1/10 of the base learning rate, while all other components use the full LR.
+This LR ratio is crucial for achieving strong and stable finetuning performance. This is already done for you by default.
+❕Note
+
+Completely matching the official reported performance may require an additional warm-up LR schedule for soft-prompts, which can bring minor improvements.
+We encourage implementing this in your customized training pipeline for optimal results.
+
+## Core Concepts
+
+### 1. Action Modes
+
+X-VLA uses an **Action Registry** system to handle different action spaces and embodiments. The `action_mode` parameter defines how actions are processed, what loss functions are used, and how predictions are post-processed.
+
+#### Available Action Modes
+
+| Action Mode      | Action Dim              | Description                                 | Use Case                             |
+| ---------------- | ----------------------- | ------------------------------------------- | ------------------------------------ |
+| `ee6d`           | 20                      | End-effector with xyz, 6D rotation, gripper | Dual-arm setups with spatial control |
+| `joint`          | 14                      | Joint-space with gripper                    | Direct joint control robots          |
+| `agibot_ee6d`    | 20                      | AGI-bot variant with MSE loss               | AGI-bot platforms                    |
+| `so101_bimanual` | 20 (model), 12 (real)   | SO101 bimanual robot                        | Bimanual manipulation tasks          |
+| `auto`           | 20 (model), auto (real) | Auto-detects action dim from dataset        | **Recommended** for new robots       |
+
+#### Why Action Modes Matter
+
+When you have a pretrained checkpoint like `lerobot/xvla-base` trained with `action_dim=20`, and you want to train on a dataset with a different action dimension (e.g., 14 for bimanual arms), you can't simply trim the action dimension. The action mode orchestrates:
+
+1. **Loss Computation**: Different loss functions for different action components (MSE for joints, BCE for grippers, etc.)
+2. **Preprocessing**: Zeroing out gripper channels, padding dimensions
+3. **Postprocessing**: Applying sigmoid to gripper logits, trimming padding
+
+#### Example: BimanualSO101 Action Space
+
+The `so101_bimanual` action mode handles the mismatch between model output (20D) and real robot control (12D):
+
+```python
+# Model outputs 20 dimensions for compatibility
+dim_action = 20
+
+# Real robot only needs 12 dimensions
+# [left_arm (6), right_arm (6)] = [joints (5) + gripper (1)] × 2
+REAL_DIM = 12
+
+# Preprocessing: Pad 12D actions to 20D for training
+# Postprocessing: Trim 20D predictions to 12D for deployment
+```
+
+See the [action_hub.py](/home/jade_choghari/robot/lerobot/src/lerobot/policies/xvla/action_hub.py) implementation for details.
+
+#### Auto Action Mode (Recommended)
+
+The `auto` action mode is the easiest way to use X-VLA with any robot. It automatically detects your dataset's action dimension and handles padding/trimming:
+
+```bash
+lerobot-train \
+  --policy.path="lerobot/xvla-base" \
+  --policy.action_mode=auto \
+  --policy.max_action_dim=20 \
+  ...
+```
+
+**How it works:**
+
+- Reads `action_feature.shape[-1]` from your dataset (e.g., 7 for Franka)
+- Model outputs `max_action_dim` (default 20) for pretrained compatibility
+- Loss is computed **only on the real dimensions**: `MSE(pred[:,:,:real_dim], target[:,:,:real_dim])`
+- Postprocess trims output back to `real_dim` for robot control
+
+This eliminates the need to create custom action modes for most robots.
+
+### 2. Domain IDs
+
+Domain IDs are learnable identifiers for different robot configurations and camera setups. They allow X-VLA to distinguish between:
+
+- Different robots (Robot 1 vs Robot 2)
+- Different camera configurations (cam1 vs cam2)
+- Different combinations (Robot1-cam1-cam2 vs Robot1-cam1 vs Robot2-cam1)
+
+#### Setting Domain IDs
+
+**During Training**: By default, domain_id is set to 0 for general training.
+
+**During Evaluation**: Specify the domain_id that matches your checkpoint's training configuration.
+
+```python
+# Example: LIBERO checkpoint uses domain_id=3
+domain_id = 3
+```
+
+The domain_id is automatically added to observations by the `XVLAAddDomainIdProcessorStep` in the preprocessing pipeline.
+
+The `lerobot/xvla-base` model has been trained on the following domain IDs. It is recommended to choose one that most resembles your robot/configuration:
+
+#### Fine-tuning Datasets
+
+| Dataset Name     | Domain ID |
+| ---------------- | --------- |
+| Bridge           | 0         |
+| RT1              | 1         |
+| Calvin           | 2         |
+| libero           | 3         |
+| widowx-air       | 4         |
+| AIR-AGILEX-HQ    | 5         |
+| robotwin2_abs_ee | 6         |
+| robotwin2_clean  | 6         |
+| robocasa-human   | 7         |
+| VLABench         | 8         |
+| AGIBOT-challenge | 9         |
+| AIR-AGILEX       | 10        |
+| AIRBOT           | 18        |
+
+### 3. Processor Steps
+
+X-VLA requires specific preprocessing and postprocessing steps for proper operation.
+
+#### Required Preprocessing Steps
+
+1. **XVLAImageToFloatProcessorStep**: Converts images from [0, 255] to [0, 1] range
+2. **XVLAImageNetNormalizeProcessorStep**: Applies ImageNet normalization (required for VLM backbone)
+3. **XVLAAddDomainIdProcessorStep**: Adds domain_id to observations
+
+#### Example Custom Processor
+
+For LIBERO environments, a custom processor handles the specific observation format:
+
+```python
+from lerobot.policies.xvla.processor_xvla import LiberoProcessorStep
+
+processor = LiberoProcessorStep()
+# Handles robot_state dictionary, converts rotation matrices to 6D representation
+# Applies 180° image rotation for camera convention
+```
+
+### 4. Configuration Parameters
+
+Key configuration parameters for X-VLA:
+
+```python
+# Observation and action
+n_obs_steps: int = 1          # Number of observation timesteps
+chunk_size: int = 32           # Action sequence length
+n_action_steps: int = 32       # Number of action steps to execute
+
+# Model architecture
+hidden_size: int = 1024        # Transformer hidden dimension
+depth: int = 24                # Number of transformer layers
+num_heads: int = 16            # Number of attention heads
+num_domains: int = 30          # Maximum number of domain IDs
+len_soft_prompts: int = 32     # Length of soft prompt embeddings
+
+# Action space
+action_mode: str = "ee6d"      # Action space type (use "auto" for auto-detection)
+use_proprio: bool = True       # Use proprioceptive state
+max_state_dim: int = 32        # Maximum state dimension
+max_action_dim: int = 20       # Max action dim for padding (used by "auto" mode)
+
+# Vision
+num_image_views: int | None    # Number of camera views
+resize_imgs_with_padding: tuple[int, int] | None  # Target image size with padding
+
+# Training
+num_denoising_steps: int = 10  # Flow matching denoising steps
+```
+
+## Creating Custom Action Modes
+
+If your robot has a unique action space, you can create a custom action mode:
+
+### Step 1: Define Your Action Space
+
+```python
+from lerobot.policies.xvla.action_hub import BaseActionSpace, register_action
+import torch.nn as nn
+
+@register_action("my_custom_robot")
+class MyCustomActionSpace(BaseActionSpace):
+    """Custom action space for my robot."""
+
+    dim_action = 15  # Your robot's action dimension
+    gripper_idx = (7, 14)  # Gripper channel indices
+
+    def __init__(self):
+        super().__init__()
+        self.mse = nn.MSELoss()
+        self.bce = nn.BCEWithLogitsLoss()
+
+    def compute_loss(self, pred, target):
+        """Define your loss computation."""
+        # Example: MSE for joints, BCE for grippers
+        joints_loss = self.mse(pred[:, :, :7], target[:, :, :7])
+        gripper_loss = self.bce(pred[:, :, self.gripper_idx],
+                                target[:, :, self.gripper_idx])
+
+        return {
+            "joints_loss": joints_loss,
+            "gripper_loss": gripper_loss,
+        }
+
+    def preprocess(self, proprio, action, mode="train"):
+        """Preprocess actions before training."""
+        # Example: Zero out grippers in proprioception
+        proprio_m = proprio.clone()
+        action_m = action.clone() if action is not None else None
+        proprio_m[..., self.gripper_idx] = 0.0
+        if action_m is not None:
+            action_m[..., self.gripper_idx] = 0.0
+        return proprio_m, action_m
+
+    def postprocess(self, action):
+        """Post-process predictions for deployment."""
+        # Example: Apply sigmoid to gripper logits
+        action[..., self.gripper_idx] = torch.sigmoid(action[..., self.gripper_idx])
+        return action
+```
+
+### Step 2: Use Your Custom Action Mode
+
+```bash
+lerobot-train \
+  --policy.action_mode=my_custom_robot \
+  --dataset.repo_id=YOUR_DATASET \
+  --policy.path="lerobot/xvla-base" \
+  ...
+```
+
+## Advanced Topics
+
+### Multi-Camera Support
+
+X-VLA supports multiple camera views through the `num_image_views` parameter:
+
+```python
+# Configure for 3 camera views
+policy.num_image_views=3
+
+# Add empty cameras if you have fewer physical cameras
+policy.empty_cameras=1  # Adds 1 zero-padded camera view
+```
+
+### Custom Preprocessing Pipeline
+
+Create a custom preprocessing pipeline for your environment:
+
+```python
+from lerobot.processor import PolicyProcessorPipeline
+from lerobot.policies.xvla.processor_xvla import (
+    XVLAImageToFloatProcessorStep,
+    XVLAImageNetNormalizeProcessorStep,
+    XVLAAddDomainIdProcessorStep,
+)
+
+# Build custom pipeline
+preprocessor = PolicyProcessorPipeline(
+    steps=[
+        YourCustomProcessorStep(),  # Your custom processing
+        XVLAImageToFloatProcessorStep(),  # Required: convert to float
+        XVLAImageNetNormalizeProcessorStep(),  # Required: ImageNet norm
+        XVLAAddDomainIdProcessorStep(domain_id=5),  # Your domain ID
+    ]
+)
+```
+
+### Handling Different Action Dimensions
+
+When your dataset has fewer action dimensions than the pretrained model:
+
+**Option 1 (Recommended)**: Use `auto` action mode
+
+```bash
+# Automatically detects your dataset's action dimension
+# Works with any robot without custom code
+policy.action_mode=auto
+policy.max_action_dim=20  # Match pretrained model
+```
+
+**Option 2**: Use a predefined action mode with built-in padding
+
+```python
+# Model expects 20D, dataset has 12D
+# Action mode handles padding internally
+action_mode = "so101_bimanual"  # Pads 12 → 20
+```
+
+**Option 2**: Create a custom action mode that maps dimensions explicitly
+
+```python
+@register_action("my_mapped_action")
+class MappedActionSpace(BaseActionSpace):
+    dim_action = 20
+    REAL_DIM = 12
+
+    def _pad_to_model_dim(self, x):
+        # Custom padding logic
+        ...
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Issue**: "Action dimension mismatch"
+
+- **Solution**: Check that your `action_mode` matches your robot's action space. Create a custom action mode if needed.
+
+**Issue**: "Image values outside [0, 1] range"
+
+- **Solution**: Ensure images are preprocessed with `XVLAImageToFloatProcessorStep` before normalization.
+
+**Issue**: "Domain ID not found"
+
+- **Solution**: Make sure `XVLAAddDomainIdProcessorStep` is in your preprocessing pipeline with the correct domain_id.
+
+**Issue**: "Low success rate on new embodiment"
+
+- **Solution**:
+  1. Verify your action_mode is correct
+  2. Check that soft prompts are being trained (`train_soft_prompts=True`)
+  3. Ensure proper preprocessing (ImageNet normalization, domain_id)
+  4. Consider increasing training steps
+
+**Issue**: "Out of memory during training"
+
+- **Solution**:
+  1. Reduce `chunk_size` (e.g., from 32 to 16)
+  2. Enable gradient checkpointing
+  3. Reduce batch size
+  4. Freeze more components
+
+## Citation
+
+If you use X-VLA in your research, please cite:
+
+```bibtex
+@article{zheng2025x,
+  title   = {X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model},
+  author  = {Zheng, Jinliang and Li, Jianxiong and Wang, Zhihao and Liu, Dongxiu and Kang, Xirui
+             and Feng, Yuchun and Zheng, Yinan and Zou, Jiayin and Chen, Yilun and Zeng, Jia and others},
+  journal = {arXiv preprint arXiv:2510.10274},
+  year    = {2025}
+}
+```
+
+## Additional Resources
+
+- [X-VLA Paper](https://arxiv.org/pdf/2510.10274)
+- [LeRobot Documentation](https://github.com/huggingface/lerobot)
+- [Action Registry Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/action_hub.py)
+- [Processor Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/processor_xvla.py)
+- [Model Configuration](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/configuration_xvla.py)
+
+## Contributing
+
+We welcome contributions! If you've implemented a new action mode or processor for your robot, please consider submitting a PR to help the community.

examples/backward_compatibility/replay.py

@@ -45,7 +45,7 @@ from lerobot.robots import (  # noqa: F401
     so101_follower,
 )
 from lerobot.utils.constants import ACTION
-from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.robot_utils import precise_sleep
 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
-        busy_wait(1 / dataset.fps - dt_s)
+        precise_sleep(1 / dataset.fps - dt_s)
 
     robot.disconnect()
 

examples/dataset/load_lerobot_dataset.py

@@ -34,105 +34,106 @@ 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)
 
-# 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)
+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)
 
-# Or simply explore them in your web browser directly at:
-# https://huggingface.co/datasets?other=LeRobot
+    # 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)
 
-# 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)
+    # Or simply explore them in your web browser directly at:
+    # https://huggingface.co/datasets?other=LeRobot
 
-# 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")
+    # 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)
 
-print("Tasks:")
-print(ds_meta.tasks)
-print("Features:")
-pprint(ds_meta.features)
+    # 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")
 
-# You can also get a short summary by simply printing the object:
-print(ds_meta)
+    print("Tasks:")
+    print(ds_meta.tasks)
+    print("Features:")
+    pprint(ds_meta.features)
 
-# 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 also get a short summary by simply printing the object:
+    print(ds_meta)
 
-# 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}")
+    # 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])
 
-# 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}")
+    # 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}")
 
-# The previous metadata class is contained in the 'meta' attribute of the dataset:
-print(dataset.meta)
+    # 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}")
 
-# LeRobotDataset actually wraps an underlying Hugging Face dataset
-# (see https://huggingface.co/docs/datasets for more information).
-print(dataset.hf_dataset)
+    # The previous metadata class is contained in the 'meta' attribute of the dataset:
+    print(dataset.meta)
 
-# 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]
+    # LeRobotDataset actually wraps an underlying Hugging Face dataset
+    # (see https://huggingface.co/docs/datasets for more information).
+    print(dataset.hf_dataset)
 
-# 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)]
+    # 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]
 
-# The objects returned by the dataset are all torch.Tensors
-print(type(frames[0]))
-print(frames[0].shape)
+    # 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)]
 
-# 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.
+    # The objects returned by the dataset are all torch.Tensors
+    print(type(frames[0]))
+    print(frames[0].shape)
 
-# 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.
+    # 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.
 
-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)
+    # 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)
 
-if __name__ == "__main__":
     dataloader = torch.utils.data.DataLoader(
         dataset,
         num_workers=4,
@@ -144,3 +145,7 @@ if __name__ == "__main__":
         print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
         print(f"{batch['action'].shape=}")  # (32, 64, c)
         break
+
+
+if __name__ == "__main__":
+    main()

examples/lekiwi/evaluate.py

@@ -33,83 +33,68 @@ 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")
 
-robot = LeKiwiClient(robot_config)
+def main():
+    # Create the robot configuration & robot
+    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 
-# Create policy
-policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+    robot = LeKiwiClient(robot_config)
 
-# 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 policy
+    policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
 
-# 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,
-)
+    # 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}
 
-# 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,
+    # Create the dataset
+    dataset = LeRobotDataset.create(
+        repo_id=HF_DATASET_ID,
         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=teleop_action_processor,
-        robot_action_processor=robot_action_processor,
-        robot_observation_processor=robot_observation_processor,
+        features=dataset_features,
+        robot_type=robot.name,
+        use_videos=True,
+        image_writer_threads=4,
     )
 
-    # 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")
+    # 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,
+            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,
@@ -118,21 +103,42 @@ while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
             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
+        # 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,
+            )
 
-    # Save episode
-    dataset.save_episode()
-    recorded_episodes += 1
+        if events["rerecord_episode"]:
+            log_say("Re-record episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue
 
-# Clean up
-log_say("Stop recording")
-robot.disconnect()
-listener.stop()
+        # Save episode
+        dataset.save_episode()
+        recorded_episodes += 1
 
-dataset.finalize()
-dataset.push_to_hub()
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    listener.stop()
+
+    dataset.finalize()
+    dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/lekiwi/record.py

@@ -34,78 +34,62 @@ 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()
 
-# Initialize the robot and teleoperator
-robot = LeKiwiClient(robot_config)
-leader_arm = SO100Leader(leader_arm_config)
-keyboard = KeyboardTeleop(keyboard_config)
+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()
 
-# TODO(Steven): Update this example to use pipelines
-teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    # Initialize the robot and teleoperator
+    robot = LeKiwiClient(robot_config)
+    leader_arm = SO100Leader(leader_arm_config)
+    keyboard = KeyboardTeleop(keyboard_config)
 
-# 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}
+    # TODO(Steven): Update this example to use pipelines
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
 
-# 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,
-)
+    # 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}
 
-# 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,
+    # Create the dataset
+    dataset = LeRobotDataset.create(
+        repo_id=HF_REPO_ID,
         fps=FPS,
-        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,
+        features=dataset_features,
+        robot_type=robot.name,
+        use_videos=True,
+        image_writer_threads=4,
     )
 
-    # 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")
+    # 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,
+            dataset=dataset,
             teleop=[leader_arm, keyboard],
-            control_time_s=RESET_TIME_SEC,
+            control_time_s=EPISODE_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
             teleop_action_processor=teleop_action_processor,
@@ -113,23 +97,45 @@ while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
             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
+        # 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,
+            )
 
-    # Save episode
-    dataset.save_episode()
-    recorded_episodes += 1
+        if events["rerecord_episode"]:
+            log_say("Re-record episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue
 
-# Clean up
-log_say("Stop recording")
-robot.disconnect()
-leader_arm.disconnect()
-keyboard.disconnect()
-listener.stop()
+        # Save episode
+        dataset.save_episode()
+        recorded_episodes += 1
 
-dataset.finalize()
-dataset.push_to_hub()
+    # 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()

examples/lekiwi/replay.py

@@ -20,42 +20,48 @@ 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 busy_wait
+from lerobot.utils.robot_utils import precise_sleep
 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")
 
-# Initialize the robot
-robot = LeKiwiClient(robot_config)
+def main():
+    # Initialize the robot config
+    robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
 
-# 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)
+    # Initialize the robot
+    robot = LeKiwiClient(robot_config)
 
-# Connect to the robot
-robot.connect()
+    # 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)
 
-if not robot.is_connected:
-    raise ValueError("Robot is not connected!")
+    # Connect to the robot
+    robot.connect()
 
-print("Starting replay loop...")
-log_say(f"Replaying episode {EPISODE_IDX}")
-for idx in range(len(episode_frames)):
-    t0 = time.perf_counter()
+    if not robot.is_connected:
+        raise ValueError("Robot is not connected!")
 
-    # Get recorded action from dataset
-    action = {
-        name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-    }
+    print("Starting replay loop...")
+    log_say(f"Replaying episode {EPISODE_IDX}")
+    for idx in range(len(episode_frames)):
+        t0 = time.perf_counter()
 
-    # Send action to robot
-    _ = robot.send_action(action)
+        # Get recorded action from dataset
+        action = {
+            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+        }
 
-    busy_wait(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+        # Send action to robot
+        _ = robot.send_action(action)
 
-robot.disconnect()
+        precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+
+    robot.disconnect()
+
+
+if __name__ == "__main__":
+    main()

examples/lekiwi/teleoperate.py

@@ -19,54 +19,60 @@ 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 busy_wait
+from lerobot.utils.robot_utils import precise_sleep
 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")
 
-# Initialize the robot and teleoperator
-robot = LeKiwiClient(robot_config)
-leader_arm = SO100Leader(teleop_arm_config)
-keyboard = KeyboardTeleop(keyboard_config)
+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")
 
-# 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()
+    # Initialize the robot and teleoperator
+    robot = LeKiwiClient(robot_config)
+    leader_arm = SO100Leader(teleop_arm_config)
+    keyboard = KeyboardTeleop(keyboard_config)
 
-# Init rerun viewer
-init_rerun(session_name="lekiwi_teleop")
+    # 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()
 
-if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-    raise ValueError("Robot or teleop is not connected!")
+    # Init rerun viewer
+    init_rerun(session_name="lekiwi_teleop")
 
-print("Starting teleop loop...")
-while True:
-    t0 = time.perf_counter()
+    if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+        raise ValueError("Robot or teleop is not connected!")
 
-    # Get robot observation
-    observation = robot.get_observation()
+    print("Starting teleop loop...")
+    while True:
+        t0 = time.perf_counter()
 
-    # 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 robot observation
+        observation = robot.get_observation()
 
-    action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
+        # 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)
 
-    # Send action to robot
-    _ = robot.send_action(action)
+        action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
 
-    # Visualize
-    log_rerun_data(observation=observation, action=action)
+        # Send action to robot
+        _ = robot.send_action(action)
 
-    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+        # Visualize
+        log_rerun_data(observation=observation, action=action)
+
+        precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+
+
+if __name__ == "__main__":
+    main()

examples/openarms/debug_can_communication.py

@@ -0,0 +1,416 @@
+#!/usr/bin/env python3
+"""
+Comprehensive debug script for OpenArms CAN FD communication.
+Tests all 4 CAN interfaces with CAN FD support.
+"""
+
+import can
+import time
+import sys
+import subprocess
+
+def check_can_interface(port):
+    """Check if CAN interface is UP and configured."""
+    try:
+        result = subprocess.run(['ip', 'link', 'show', port], 
+                              capture_output=True, text=True)
+        if result.returncode != 0:
+            return False, "Interface not found", None
+        
+        output = result.stdout
+        if 'UP' not in output:
+            return False, "Interface is DOWN", None
+        
+        # Check if CAN FD is enabled
+        is_fd = 'fd on' in output.lower() or 'canfd' in output.lower()
+        
+        return True, "Interface is UP", is_fd
+    except FileNotFoundError:
+        return None, "Cannot check (ip command not found)", None
+
+
+def test_motor_on_interface(bus, motor_id, timeout=2.0, use_fd=False):
+    """
+    Test a single motor and return all responses.
+    
+    Returns:
+        list of (arbitration_id, data) tuples for all responses received
+    """
+    # Send enable command
+    enable_msg = can.Message(
+        arbitration_id=motor_id,
+        data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC],
+        is_extended_id=False,
+        is_fd=use_fd
+    )
+    
+    try:
+        bus.send(enable_msg)
+    except Exception as e:
+        return None, f"Send error: {e}"
+    
+    # Listen for responses
+    responses = []
+    start_time = time.time()
+    
+    while time.time() - start_time < timeout:
+        msg = bus.recv(timeout=0.1)
+        if msg:
+            responses.append((msg.arbitration_id, msg.data, msg.is_fd if hasattr(msg, 'is_fd') else False))
+    
+    # Send disable command
+    disable_msg = can.Message(
+        arbitration_id=motor_id,
+        data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFD],
+        is_extended_id=False,
+        is_fd=use_fd
+    )
+    try:
+        bus.send(disable_msg)
+    except:
+        pass
+    
+    return responses, None
+
+
+def test_interface(port, interface_type="socketcan", use_can_fd=True):
+    """Test all 8 motors on a single CAN interface."""
+    
+    results = {
+        'interface': port,
+        'status': None,
+        'is_fd': use_can_fd,
+        'motors': {}
+    }
+    
+    # Check interface status
+    status_ok, status_msg, interface_has_fd = check_can_interface(port)
+    
+    if interface_has_fd is not None:
+        results['interface_fd_enabled'] = interface_has_fd
+        if use_can_fd and not interface_has_fd:
+            status_msg += " (CAN FD NOT enabled on interface!)"
+        elif interface_has_fd:
+            status_msg += " (CAN FD enabled)"
+    
+    results['status'] = status_msg
+    
+    if status_ok is False:
+        return results
+    
+    # Try to connect
+    try:
+        if use_can_fd:
+            print(f"  Connecting to {port} with CAN FD (1 Mbps / 5 Mbps)...")
+            bus = can.interface.Bus(
+                channel=port,
+                interface=interface_type,
+                bitrate=1000000,
+                data_bitrate=5000000,
+                fd=True
+            )
+        else:
+            print(f"  Connecting to {port} with CAN 2.0 (1 Mbps)...")
+            bus = can.interface.Bus(
+                channel=port,
+                interface=interface_type,
+                bitrate=1000000
+            )
+    except Exception as e:
+        results['status'] = f"Connection failed: {e}"
+        return results
+    
+    try:
+        # Clear any pending messages
+        while bus.recv(timeout=0.01):
+            pass
+        
+        # Test each motor (0x01 to 0x08)
+        for motor_id in range(0x01, 0x09):
+            responses, error = test_motor_on_interface(bus, motor_id, timeout=1.0, use_fd=use_can_fd)
+            
+            if error:
+                results['motors'][motor_id] = {'error': error}
+            elif responses:
+                results['motors'][motor_id] = {
+                    'found': True,
+                    'responses': responses
+                }
+            else:
+                results['motors'][motor_id] = {
+                    'found': False,
+                    'responses': []
+                }
+            
+            time.sleep(0.05)  # Small delay between motors
+        
+    finally:
+        bus.shutdown()
+    
+    return results
+
+
+def print_results(all_results):
+    """Print formatted results for all interfaces."""
+    
+    print("SUMMARY - Motors Found on Each Interface")
+    
+    motor_names = {
+        0x01: "joint_1 (Shoulder pan)",
+        0x02: "joint_2 (Shoulder lift)",
+        0x03: "joint_3 (Shoulder rotation)",
+        0x04: "joint_4 (Elbow flex)",
+        0x05: "joint_5 (Wrist roll)",
+        0x06: "joint_6 (Wrist pitch)",
+        0x07: "joint_7 (Wrist rotation)",
+        0x08: "gripper",
+    }
+    
+    total_found = 0
+    
+    for result in all_results:
+        interface = result['interface']
+        status = result['status']
+        
+        print(f"{interface}: {status}")
+        if result.get('is_fd'):
+            print(f"  Mode: CAN FD")
+        else:
+            print(f"  Mode: CAN 2.0")
+        
+        if 'Connection failed' in status or 'DOWN' in status:
+            print(f"  ⚠ Cannot test {interface}")
+            continue
+        
+        motors_found = 0
+        
+        for motor_id in range(0x01, 0x09):
+            motor_data = result['motors'].get(motor_id, {})
+            motor_name = motor_names.get(motor_id, "Unknown")
+            
+            if motor_data.get('error'):
+                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✗ {motor_data['error']}")
+            elif motor_data.get('found'):
+                motors_found += 1
+                total_found += 1
+                responses = motor_data['responses']
+                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✓ FOUND")
+                
+                for resp_id, data, is_fd in responses:
+                    data_hex = data.hex()
+                    fd_flag = " [FD]" if is_fd else " [2.0]"
+                    print(f"    → Response from 0x{resp_id:02X}{fd_flag}: {data_hex}")
+            else:
+                print(f"  Motor 0x{motor_id:02X} ({motor_name}): ✗ No response")
+        
+        print(f"\n  Summary: {motors_found}/8 motors found on {interface}")
+    
+    # Overall summary
+    print("OVERALL SUMMARY")
+    print(f"Total motors found across all interfaces: {total_found}")
+    
+    # Analyze configuration
+    print("DIAGNOSIS")
+    
+    for result in all_results:
+        interface = result['interface']
+        motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
+        
+        if motors_found == 0:
+            print(f"\n⚠ {interface}: NO MOTORS FOUND")
+            print("  Possible issues:")
+            print("    1. CAN FD mode mismatch (interface vs motor configuration)")
+            print("    2. Missing 120Ω termination resistors at BOTH cable ends")
+            print("    3. Motor timeout parameter set incorrectly (should NOT be 0)")
+            print("    4. CANH/CANL wiring issue")
+            print("    5. Cable too long (>40m for CAN FD at 5Mbps)")
+            
+            # Check FD mismatch
+            if result.get('is_fd') and not result.get('interface_fd_enabled'):
+                print("    ⚠️ CRITICAL: Trying CAN FD but interface NOT configured for FD!")
+                print(f"       Fix: sudo ip link set {interface} type can bitrate 1000000 dbitrate 5000000 fd on")
+                
+        elif motors_found < 8:
+            print(f"\n⚠ {interface}: Only {motors_found}/8 motors responding")
+            print("  Check power and connections for missing motors")
+        else:
+            print(f"\n✓ {interface}: All 8 motors responding correctly!")
+    
+    # Check for unexpected response IDs
+    print("RESPONSE ID ANALYSIS")
+    
+    for result in all_results:
+        interface = result['interface']
+        unexpected = []
+        
+        for motor_id, motor_data in result['motors'].items():
+            if motor_data.get('found'):
+                expected_id = motor_id + 0x10
+                actual_ids = [resp[0] for resp in motor_data['responses']]
+                
+                if expected_id not in actual_ids:
+                    unexpected.append((motor_id, actual_ids))
+        
+        if unexpected:
+            print(f"\n⚠ {interface}: Unexpected response IDs detected")
+            for motor_id, actual_ids in unexpected:
+                expected_id = motor_id + 0x10
+                print(f"  Motor 0x{motor_id:02X}: Expected 0x{expected_id:02X}, "
+                      f"got {[f'0x{id:02X}' for id in actual_ids]}")
+            print("  → Motor Master IDs need reconfiguration")
+        else:
+            motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
+            if motors_found > 0:
+                print(f"\n✓ {interface}: All responding motors use correct IDs")
+
+
+def test_communication_speed(interface, motor_id, num_iterations=100):
+    """
+    Test communication speed with a motor.
+    
+    Returns:
+        tuple: (hz, avg_latency_ms) or (None, None) if test failed
+    """
+    try:
+        # Connect to interface
+        bus = can.interface.Bus(
+            channel=interface,
+            interface="socketcan",
+            bitrate=1000000,
+            data_bitrate=5000000,
+            fd=True
+        )
+        
+        # Send refresh commands and measure round-trip time
+        latencies = []
+        successful = 0
+        
+        for _ in range(num_iterations):
+            start = time.perf_counter()
+            
+            # Send enable command (lightweight operation)
+            enable_msg = can.Message(
+                arbitration_id=motor_id,
+                data=[0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFC],
+                is_extended_id=False,
+                is_fd=True
+            )
+            bus.send(enable_msg)
+            
+            # Wait for response
+            msg = bus.recv(timeout=0.1)
+            
+            if msg:
+                latency = (time.perf_counter() - start) * 1000  # Convert to ms
+                latencies.append(latency)
+                successful += 1
+        
+        bus.shutdown()
+        
+        if successful > 0:
+            avg_latency = sum(latencies) / len(latencies)
+            hz = 1000.0 / avg_latency if avg_latency > 0 else 0
+            return hz, avg_latency
+        
+        return None, None
+        
+    except Exception as e:
+        print(f"    Speed test error: {e}")
+        return None, None
+
+
+def main():
+    """Main function to test all CAN interfaces with CAN FD."""
+    
+    print("\nThis will test all 4 CAN interfaces (can0-can3) with CAN FD")
+    print("Testing motors 0x01-0x08 on each interface")
+    print()
+    print("Make sure:")
+    print("  ✓ Motors are powered (24V)")
+    print("  ✓ CAN interfaces configured with FD mode:")
+    print("    ./examples/openarms/setup_can.sh")
+    print("  ✓ Motor 'timeout' parameter NOT set to 0 (use Damiao tools)")
+    print("  ✓ CAN wiring includes 120Ω termination at BOTH ends")
+    print()
+    
+    input("Press ENTER to start testing...")
+    
+    # Test all 4 interfaces with CAN FD
+    all_results = []
+    
+    for i in range(4):
+        interface = f"can{i}"
+        print(f"Testing {interface}...")
+        
+        result = test_interface(interface, use_can_fd=True)
+        all_results.append(result)
+        
+        # Quick status
+        if 'Connection failed' in result['status'] or 'DOWN' in result['status']:
+            print(f"  ⚠ {interface}: {result['status']}")
+        else:
+            motors_found = sum(1 for m in result['motors'].values() if m.get('found'))
+            print(f"  {interface}: {motors_found}/8 motors found")
+        
+        time.sleep(0.2)
+    
+    # Print detailed results
+    print_results(all_results)
+    
+    print("Testing Complete!")
+    
+    all_found = sum(sum(1 for m in r['motors'].values() if m.get('found')) for r in all_results)
+    
+    if all_found == 0:
+        print("\n⚠️ CRITICAL: No motors found on any interface!")
+        print("\nTop issues to check:")
+        print("  1. Motor 'timeout' parameter (use Damiao tools to set > 0)")
+        print("  2. CAN FD not enabled (run ./examples/openarms/setup_can.sh)")
+        print("  3. Missing termination resistors")
+        print("\nTry:")
+        print("  a) Check motor parameters with Damiao Debugging Tools")
+        print("  b) Verify CAN FD is enabled: ip -d link show can0 | grep fd")
+        print("  c) Run setup script: ./examples/openarms/setup_can.sh")
+    else:
+        # Run speed test on interfaces with motors
+        print("COMMUNICATION SPEED TEST")
+        print("\nTesting maximum communication frequency...")
+        
+        for result in all_results:
+            interface = result['interface']
+            
+            # Find first responding motor
+            responding_motor = None
+            for motor_id, motor_data in result['motors'].items():
+                if motor_data.get('found'):
+                    responding_motor = motor_id
+                    break
+            
+            if responding_motor:
+                print(f"\n{interface}: Testing with motor 0x{responding_motor:02X}...")
+                hz, latency = test_communication_speed(interface, responding_motor, num_iterations=100)
+                
+                if hz:
+                    print(f"  ✓ Max frequency: {hz:.1f} Hz")
+                    print(f"  ✓ Avg latency: {latency:.2f} ms")
+                    print(f"  ✓ Commands per second: ~{int(hz)}")
+                else:
+                    print(f"  ✗ Speed test failed")
+            else:
+                print(f"\n{interface}: No motors found, skipping speed test")
+
+        print()
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\n\nTesting interrupted by user.")
+        sys.exit(1)
+    except Exception as e:
+        print(f"\nUnexpected error: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+

examples/openarms/evaluate.py

@@ -0,0 +1,360 @@
+#!/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.
+
+"""
+OpenArms Policy Evaluation
+
+Evaluates a trained policy on the OpenArms robot by running inference and recording
+the evaluation episodes to a dataset. Supports optional leader arm for manual resets.
+
+Example usage:
+    python examples/openarms/evaluate.py
+"""
+
+import time
+from pathlib import Path
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import combine_feature_dicts
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.processor import make_default_processors
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun
+
+
+HF_MODEL_ID = "lerobot-data-collection/three-folds-pi0"  # TODO: Replace with your trained model
+HF_EVAL_DATASET_ID = "lerobot-data-collection/three-folds-pi0_eval7"  # TODO: Replace with your eval dataset name
+TASK_DESCRIPTION = "three-folds-dataset"  # TODO: Replace with your task, this should match!!
+
+NUM_EPISODES = 1
+FPS = 30
+EPISODE_TIME_SEC = 300
+RESET_TIME_SEC = 60
+
+# Robot CAN interfaces
+FOLLOWER_LEFT_PORT = "can0"
+FOLLOWER_RIGHT_PORT = "can1"
+
+# If enabled, you can manually reset the environment between evaluation episodes
+USE_LEADER_FOR_RESETS = True  # Set to False if you don't want to use leader
+LEADER_LEFT_PORT = "can2"
+LEADER_RIGHT_PORT = "can3"
+
+# Camera configuration
+CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=FPS),
+}
+
+def main():
+    """Main evaluation function."""
+    print("OpenArms Policy Evaluation")
+    print(f"\nModel: {HF_MODEL_ID}")
+    print(f"Evaluation Dataset: {HF_EVAL_DATASET_ID}")
+    print(f"Task: {TASK_DESCRIPTION}")
+    print(f"Episodes: {NUM_EPISODES}")
+    print(f"Episode Duration: {EPISODE_TIME_SEC}s")
+    print(f"Reset Duration: {RESET_TIME_SEC}s")
+    print(f"Use Leader for Resets: {USE_LEADER_FOR_RESETS}")
+    
+    follower_config = OpenArmsFollowerConfig(
+        port_left=FOLLOWER_LEFT_PORT,
+        port_right=FOLLOWER_RIGHT_PORT,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=CAMERA_CONFIG,
+    )
+    
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+    
+    if not follower.is_connected:
+        raise RuntimeError("Follower robot failed to connect!")
+
+    
+    leader = None
+    if USE_LEADER_FOR_RESETS:
+        leader_config = OpenArmsLeaderConfig(
+            port_left=LEADER_LEFT_PORT,
+            port_right=LEADER_RIGHT_PORT,
+            can_interface="socketcan",
+            id="openarms_leader",
+            manual_control=False,  # Enable torque control for gravity compensation
+        )
+        
+        leader = OpenArmsLeader(leader_config)
+        leader.connect(calibrate=False)
+        
+        if not leader.is_connected:
+            raise RuntimeError("Leader robot failed to connect!")
+        
+        # Enable gravity compensation
+        if leader.pin_robot is not None:
+            leader.bus_right.enable_torque()
+            leader.bus_left.enable_torque()
+            time.sleep(0.1)
+            print(f"Leader connected with gravity compensation ({LEADER_LEFT_PORT}, {LEADER_RIGHT_PORT})")
+        else:
+            print(f"Leader connected but gravity compensation unavailable (no URDF)")
+
+    # Build default processors for action and observation
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    
+    # Build dataset features from robot features and processors
+    # For actions, only include positions (no velocity or torque)
+    action_features_hw = {}
+    for key, value in follower.action_features.items():
+        if key.endswith(".pos"):
+            action_features_hw[key] = value
+    
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+    
+    # Check if dataset already exists
+    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / HF_EVAL_DATASET_ID
+    if dataset_path.exists():
+        print(f"Evaluation dataset already exists at: {dataset_path}")
+        print("This will append new episodes to the existing dataset.")
+        choice = input("  Continue? (y/n): ").strip().lower()
+        if choice != 'y':
+            print("  Aborting evaluation.")
+            follower.disconnect()
+            if leader:
+                leader.disconnect()
+            return
+    
+    # Create dataset
+    dataset = LeRobotDataset.create(
+        repo_id=HF_EVAL_DATASET_ID,
+        fps=FPS,
+        features=dataset_features,
+        robot_type=follower.name,
+        use_videos=True,
+        image_writer_processes=0,
+        image_writer_threads=12, 
+    )
+    
+    # Load policy config from pretrained model and create policy using factory
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+    policy = make_policy(policy_config, ds_meta=dataset.meta)
+    
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy.config,
+        pretrained_path=HF_MODEL_ID,
+        dataset_stats=dataset.meta.stats,
+        preprocessor_overrides={
+            "device_processor": {"device": str(policy.config.device)}
+        },
+    )
+
+    print(f"\nRunning evaluation...")
+    # Initialize keyboard listener and visualization
+    listener, events = init_keyboard_listener()
+    init_rerun(session_name="openarms_evaluation")
+    episode_idx = 0
+    
+    try:
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Evaluating episode {episode_idx + 1} of {NUM_EPISODES}")
+            print(f"\nRunning inference for episode {episode_idx + 1}...")
+            
+            # Run inference with policy
+            record_loop(
+                robot=follower,
+                events=events,
+                fps=FPS,
+                teleop_action_processor=teleop_action_processor,
+                robot_action_processor=robot_action_processor,
+                robot_observation_processor=robot_observation_processor,
+                policy=policy,
+                preprocessor=preprocessor,
+                postprocessor=postprocessor,
+                dataset=dataset,
+                control_time_s=EPISODE_TIME_SEC,
+                single_task=TASK_DESCRIPTION,
+                display_data=True,
+            )
+            
+            # Handle re-recording
+            if events["rerecord_episode"]:
+                log_say("Re-recording episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
+            
+            # Save episode
+            if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                print(f"Saving episode {episode_idx + 1} ({dataset.episode_buffer['size']} frames)...")
+                dataset.save_episode()
+                episode_idx += 1
+            
+            # Reset environment between episodes (if not last episode)
+            if not events["stop_recording"] and episode_idx < NUM_EPISODES:
+                if USE_LEADER_FOR_RESETS and leader:
+                    log_say("Reset the environment using leader arms")
+                    print(f"\nManual reset period ({RESET_TIME_SEC}s)...")
+                    
+                    # Use leader for manual reset with gravity compensation
+                    import numpy as np
+                    
+                    dt = 1 / FPS
+                    reset_start_time = time.perf_counter()
+                    
+                    while time.perf_counter() - reset_start_time < RESET_TIME_SEC:
+                        if events["exit_early"] or events["stop_recording"]:
+                            break
+                        
+                        loop_start = time.perf_counter()
+                        
+                        # Get leader state
+                        leader_action = leader.get_action()
+                        
+                        # Extract positions and velocities
+                        leader_positions_deg = {}
+                        leader_velocities_deg_per_sec = {}
+                        
+                        for motor in leader.bus_right.motors:
+                            pos_key = f"right_{motor}.pos"
+                            vel_key = f"right_{motor}.vel"
+                            if pos_key in leader_action:
+                                leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
+                            if vel_key in leader_action:
+                                leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
+                        
+                        for motor in leader.bus_left.motors:
+                            pos_key = f"left_{motor}.pos"
+                            vel_key = f"left_{motor}.vel"
+                            if pos_key in leader_action:
+                                leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
+                            if vel_key in leader_action:
+                                leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
+                        
+                        # Calculate gravity and friction torques
+                        leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
+                        leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
+                        
+                        leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
+                        leader_friction_torques_nm = leader._friction_from_velocity(
+                            leader_velocities_rad_per_sec,
+                            friction_scale=1.0
+                        )
+                        
+                        # Combine torques
+                        leader_total_torques_nm = {}
+                        for motor_name in leader_gravity_torques_nm:
+                            gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
+                            friction = leader_friction_torques_nm.get(motor_name, 0.0)
+                            leader_total_torques_nm[motor_name] = gravity + friction
+                        
+                        # Apply compensation
+                        for motor in leader.bus_right.motors:
+                            full_name = f"right_{motor}"
+                            position = leader_positions_deg.get(full_name, 0.0)
+                            torque = leader_total_torques_nm.get(full_name, 0.0)
+                            kd = leader.get_damping_kd(motor)
+                            
+                            leader.bus_right._mit_control(
+                                motor=motor, kp=0.0, kd=kd,
+                                position_degrees=position,
+                                velocity_deg_per_sec=0.0,
+                                torque=torque,
+                            )
+                        
+                        for motor in leader.bus_left.motors:
+                            full_name = f"left_{motor}"
+                            position = leader_positions_deg.get(full_name, 0.0)
+                            torque = leader_total_torques_nm.get(full_name, 0.0)
+                            kd = leader.get_damping_kd(motor)
+                            
+                            leader.bus_left._mit_control(
+                                motor=motor, kp=0.0, kd=kd,
+                                position_degrees=position,
+                                velocity_deg_per_sec=0.0,
+                                torque=torque,
+                            )
+                        
+                        # Send leader positions to follower
+                        follower_action = {}
+                        for joint in leader_positions_deg.keys():
+                            pos_key = f"{joint}.pos"
+                            if pos_key in leader_action:
+                                follower_action[pos_key] = leader_action[pos_key]
+                        
+                        if follower_action:
+                            follower.send_action(follower_action)
+                        
+                        # Maintain loop rate
+                        loop_duration = time.perf_counter() - loop_start
+                        sleep_time = dt - loop_duration
+                        if sleep_time > 0:
+                            time.sleep(sleep_time)
+                    
+                    print("Reset complete")
+                else:
+                    log_say("Waiting for manual reset")
+                    print(f"Manually reset the environment and press ENTER to continue")
+                    input("Press ENTER when ready...")
+        
+        print(f"Evaluation complete! {episode_idx} episodes recorded")
+        log_say("Evaluation complete", blocking=True)
+    
+    except KeyboardInterrupt:
+        print("\n\nEvaluation interrupted by user")
+    
+    finally:
+        if leader:
+            leader.bus_right.disable_torque()
+            leader.bus_left.disable_torque()
+            time.sleep(0.1)
+            leader.disconnect()
+
+        follower.disconnect()
+        
+        if listener is not None:
+            listener.stop()
+        
+        dataset.finalize()
+        print("\nUploading to Hugging Face Hub...")
+        dataset.push_to_hub(private=True)
+
+
+if __name__ == "__main__":
+    main()
+

examples/openarms/evaluate_with_rtc.py

@@ -0,0 +1,653 @@
+#!/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.
+
+"""
+OpenArms Policy Evaluation with Real-Time Chunking (RTC)
+
+Evaluates a trained policy on the OpenArms robot using RTC for smooth, continuous motion.
+RTC enables large flow-matching policies (Pi0, Pi0.5, SmolVLA) to produce reactive motion
+despite high inference latency by asynchronously generating action chunks.
+
+Features:
+- Thread-based asynchronous action generation and execution
+- RTC for smooth transitions between action chunks
+- Dataset recording for evaluation episodes
+
+Example usage:
+    python examples/openarms/evaluate_with_rtc.py
+
+    # With custom RTC parameters
+    python examples/openarms/evaluate_with_rtc.py \
+        --rtc.execution_horizon=12 \
+        --rtc.max_guidance_weight=10.0
+"""
+
+import logging
+import math
+import sys
+import time
+import traceback
+from dataclasses import dataclass, field
+from pathlib import Path
+from threading import Event, Lock, Thread
+
+import torch
+from torch import Tensor
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs import parser
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import RTCAttentionSchedule
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts, hw_to_dataset_features
+from lerobot.policies.factory import get_policy_class, make_pre_post_processors
+from lerobot.policies.rtc.action_queue import ActionQueue
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.policies.rtc.latency_tracker import LatencyTracker
+from lerobot.processor import make_default_processors
+from lerobot.rl.process import ProcessSignalHandler
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.utils.hub import HubMixin
+from lerobot.utils.utils import init_logging, log_say
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# ============================================================================
+# Default Configuration Constants
+# ============================================================================
+
+DEFAULT_HF_MODEL_ID = "lerobot-data-collection/three-folds-pi0"
+DEFAULT_HF_EVAL_DATASET_ID = "lerobot-data-collection/three-folds-pi0_eval_rtc"
+DEFAULT_TASK_DESCRIPTION = "three-folds-dataset"
+
+DEFAULT_NUM_EPISODES = 1
+DEFAULT_FPS = 30
+DEFAULT_EPISODE_TIME_SEC = 300
+DEFAULT_RESET_TIME_SEC = 60
+
+DEFAULT_FOLLOWER_LEFT_PORT = "can0"
+DEFAULT_FOLLOWER_RIGHT_PORT = "can1"
+
+DEFAULT_CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=DEFAULT_FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=DEFAULT_FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=DEFAULT_FPS),
+}
+
+
+# ============================================================================
+# Thread-Safe Robot Wrapper
+# ============================================================================
+
+
+class RobotWrapper:
+    """Thread-safe wrapper for robot operations."""
+
+    def __init__(self, robot: OpenArmsFollower):
+        self.robot = robot
+        self.lock = Lock()
+
+    def get_observation(self) -> dict[str, Tensor]:
+        with self.lock:
+            return self.robot.get_observation()
+
+    def send_action(self, action: dict) -> None:
+        with self.lock:
+            self.robot.send_action(action)
+
+    @property
+    def observation_features(self) -> dict:
+        with self.lock:
+            return self.robot.observation_features
+
+    @property
+    def action_features(self) -> dict:
+        with self.lock:
+            return self.robot.action_features
+
+    @property
+    def name(self) -> str:
+        return self.robot.name
+
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+
+@dataclass
+class OpenArmsRTCEvalConfig(HubMixin):
+    """Configuration for OpenArms evaluation with RTC."""
+
+    policy: PreTrainedConfig | None = None
+
+    rtc: RTCConfig = field(
+        default_factory=lambda: RTCConfig(
+            enabled=True,
+            execution_horizon=10,
+            max_guidance_weight=10.0,
+            prefix_attention_schedule=RTCAttentionSchedule.EXP,
+        )
+    )
+
+    model_id: str = DEFAULT_HF_MODEL_ID
+    eval_dataset_id: str = DEFAULT_HF_EVAL_DATASET_ID
+    task: str = DEFAULT_TASK_DESCRIPTION
+
+    num_episodes: int = DEFAULT_NUM_EPISODES
+    fps: float = DEFAULT_FPS
+    episode_time_sec: float = DEFAULT_EPISODE_TIME_SEC
+    reset_time_sec: float = DEFAULT_RESET_TIME_SEC
+
+    follower_left_port: str = DEFAULT_FOLLOWER_LEFT_PORT
+    follower_right_port: str = DEFAULT_FOLLOWER_RIGHT_PORT
+
+    device: str = "cuda"
+
+    # Should be higher than inference_delay + execution_horizon
+    action_queue_size_to_get_new_actions: int = 30
+
+    record_dataset: bool = True
+    push_to_hub: bool = True
+
+    use_torch_compile: bool = False
+    torch_compile_backend: str = "inductor"
+    torch_compile_mode: str = "default"
+    torch_compile_disable_cudagraphs: bool = True
+
+    def __post_init__(self):
+        policy_path = parser.get_path_arg("policy")
+        if policy_path:
+            cli_overrides = parser.get_cli_overrides("policy")
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            self.policy.pretrained_path = policy_path
+            self.model_id = policy_path
+        elif self.model_id:
+            self.policy = PreTrainedConfig.from_pretrained(self.model_id)
+            self.policy.pretrained_path = self.model_id
+
+    @classmethod
+    def __get_path_fields__(cls) -> list[str]:
+        return ["policy"]
+
+
+# ============================================================================
+# Action Generation Thread
+# ============================================================================
+
+
+def get_actions_thread(
+    policy,
+    robot: RobotWrapper,
+    robot_observation_processor,
+    action_queue: ActionQueue,
+    shutdown_event: Event,
+    cfg: OpenArmsRTCEvalConfig,
+    episode_active: Event,
+):
+    """Thread function to asynchronously generate action chunks from the policy."""
+    try:
+        logger.info("[GET_ACTIONS] Starting action generation thread")
+
+        latency_tracker = LatencyTracker()
+        time_per_chunk = 1.0 / cfg.fps
+
+        hw_features = hw_to_dataset_features(robot.observation_features, "observation")
+        policy_device = policy.config.device
+
+        logger.info(f"[GET_ACTIONS] Loading preprocessor/postprocessor from {cfg.policy.pretrained_path}")
+
+        preprocessor, postprocessor = make_pre_post_processors(
+            policy_cfg=cfg.policy,
+            pretrained_path=cfg.policy.pretrained_path,
+            dataset_stats=None,
+            preprocessor_overrides={
+                "device_processor": {"device": cfg.device},
+            },
+        )
+
+        logger.info("[GET_ACTIONS] Preprocessor/postprocessor loaded successfully")
+
+        get_actions_threshold = cfg.action_queue_size_to_get_new_actions
+        if not cfg.rtc.enabled:
+            get_actions_threshold = 0
+
+        while not shutdown_event.is_set():
+            if not episode_active.is_set():
+                time.sleep(0.01)
+                continue
+
+            if action_queue.qsize() <= get_actions_threshold:
+                current_time = time.perf_counter()
+                action_index_before_inference = action_queue.get_action_index()
+                prev_actions = action_queue.get_left_over()
+
+                inference_latency = latency_tracker.max()
+                inference_delay = math.ceil(inference_latency / time_per_chunk) if inference_latency else 0
+
+                obs = robot.get_observation()
+                obs_processed = robot_observation_processor(obs)
+
+                obs_with_policy_features = build_dataset_frame(
+                    hw_features, obs_processed, prefix="observation"
+                )
+
+                for name in obs_with_policy_features:
+                    obs_with_policy_features[name] = torch.from_numpy(obs_with_policy_features[name])
+                    if "image" in name:
+                        obs_with_policy_features[name] = (
+                            obs_with_policy_features[name].type(torch.float32) / 255
+                        )
+                        obs_with_policy_features[name] = (
+                            obs_with_policy_features[name].permute(2, 0, 1).contiguous()
+                        )
+                    obs_with_policy_features[name] = obs_with_policy_features[name].unsqueeze(0)
+                    obs_with_policy_features[name] = obs_with_policy_features[name].to(policy_device)
+
+                obs_with_policy_features["task"] = [cfg.task]
+                obs_with_policy_features["robot_type"] = robot.name
+
+                preprocessed_obs = preprocessor(obs_with_policy_features)
+
+                actions = policy.predict_action_chunk(
+                    preprocessed_obs,
+                    inference_delay=inference_delay,
+                    prev_chunk_left_over=prev_actions,
+                )
+
+                original_actions = actions.squeeze(0).clone()
+                postprocessed_actions = postprocessor(actions).squeeze(0)
+
+                new_latency = time.perf_counter() - current_time
+                new_delay = math.ceil(new_latency / time_per_chunk)
+                latency_tracker.add(new_latency)
+
+                if cfg.action_queue_size_to_get_new_actions < cfg.rtc.execution_horizon + new_delay:
+                    logger.warning(
+                        "[GET_ACTIONS] action_queue_size_to_get_new_actions too small. "
+                        "Should be higher than inference delay + execution horizon."
+                    )
+
+                action_queue.merge(
+                    original_actions, postprocessed_actions, new_delay, action_index_before_inference
+                )
+
+                logger.debug(
+                    f"[GET_ACTIONS] Generated chunk, latency={new_latency:.3f}s, "
+                    f"delay={new_delay}, queue_size={action_queue.qsize()}"
+                )
+            else:
+                time.sleep(0.01)
+
+        logger.info("[GET_ACTIONS] Action generation thread shutting down")
+    except Exception as e:
+        logger.error(f"[GET_ACTIONS] Fatal exception: {e}")
+        logger.error(traceback.format_exc())
+        shutdown_event.set()
+        sys.exit(1)
+
+
+# ============================================================================
+# Action Execution Thread
+# ============================================================================
+
+
+def actor_thread(
+    robot: RobotWrapper,
+    robot_action_processor,
+    action_queue: ActionQueue,
+    shutdown_event: Event,
+    cfg: OpenArmsRTCEvalConfig,
+    episode_active: Event,
+    dataset: LeRobotDataset | None,
+    dataset_lock: Lock,
+    teleop_action_processor,
+    robot_observation_processor,
+):
+    """Thread function to execute actions on the robot."""
+    try:
+        logger.info("[ACTOR] Starting actor thread")
+
+        action_count = 0
+        action_interval = 1.0 / cfg.fps
+        action_keys = [k for k in robot.action_features.keys() if k.endswith(".pos")]
+
+        while not shutdown_event.is_set():
+            if not episode_active.is_set():
+                time.sleep(0.01)
+                continue
+
+            start_time = time.perf_counter()
+            action = action_queue.get()
+
+            if action is not None:
+                action = action.cpu()
+
+                action_dict = {}
+                for i, key in enumerate(action_keys):
+                    if i < len(action):
+                        action_dict[key] = action[i].item()
+
+                action_processed = robot_action_processor((action_dict, None))
+                robot.send_action(action_processed)
+
+                if cfg.record_dataset and dataset is not None:
+                    with dataset_lock:
+                        obs = robot.get_observation()
+                        obs_processed = robot_observation_processor(obs)
+                        action_for_dataset = teleop_action_processor((action_dict, None))
+
+                        frame = {}
+                        for key, value in obs_processed.items():
+                            frame[f"observation.{key}"] = value
+                        for key, value in action_for_dataset.items():
+                            frame[f"action.{key}"] = value
+                        frame["task"] = cfg.task
+
+                        dataset.add_frame(frame)
+
+                action_count += 1
+
+            dt_s = time.perf_counter() - start_time
+            sleep_time = max(0, action_interval - dt_s - 0.001)
+            if sleep_time > 0:
+                time.sleep(sleep_time)
+
+        logger.info(f"[ACTOR] Actor thread shutting down. Total actions executed: {action_count}")
+    except Exception as e:
+        logger.error(f"[ACTOR] Fatal exception: {e}")
+        logger.error(traceback.format_exc())
+        shutdown_event.set()
+        sys.exit(1)
+
+
+# ============================================================================
+# Main Evaluation Function
+# ============================================================================
+
+
+def _apply_torch_compile(policy, cfg: OpenArmsRTCEvalConfig):
+    """Apply torch.compile to the policy's predict_action_chunk method."""
+    if policy.name in ["pi05", "pi0"]:
+        return policy
+
+    try:
+        if not hasattr(torch, "compile"):
+            logger.warning(
+                f"torch.compile not available. Requires PyTorch 2.0+. "
+                f"Current version: {torch.__version__}. Skipping compilation."
+            )
+            return policy
+
+        logger.info("Applying torch.compile to predict_action_chunk...")
+
+        compile_kwargs = {
+            "backend": cfg.torch_compile_backend,
+            "mode": cfg.torch_compile_mode,
+        }
+
+        if cfg.torch_compile_disable_cudagraphs:
+            compile_kwargs["options"] = {"triton.cudagraphs": False}
+
+        original_method = policy.predict_action_chunk
+        compiled_method = torch.compile(original_method, **compile_kwargs)
+        policy.predict_action_chunk = compiled_method
+        logger.info("Successfully compiled predict_action_chunk")
+
+    except Exception as e:
+        logger.error(f"Failed to apply torch.compile: {e}")
+        logger.warning("Continuing without torch.compile")
+
+    return policy
+
+
+@parser.wrap()
+def main(cfg: OpenArmsRTCEvalConfig):
+    """Main evaluation function with RTC."""
+    init_logging()
+
+    print("=" * 60)
+    print("OpenArms Policy Evaluation with RTC")
+    print("=" * 60)
+    print(f"\nModel: {cfg.model_id}")
+    print(f"Evaluation Dataset: {cfg.eval_dataset_id}")
+    print(f"Task: {cfg.task}")
+    print(f"Episodes: {cfg.num_episodes}")
+    print(f"Episode Duration: {cfg.episode_time_sec}s")
+    print(f"RTC Enabled: {cfg.rtc.enabled}")
+    print(f"RTC Execution Horizon: {cfg.rtc.execution_horizon}")
+    print(f"RTC Max Guidance Weight: {cfg.rtc.max_guidance_weight}")
+    print(f"Device: {cfg.device}")
+    print("=" * 60)
+
+    signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
+    shutdown_event = signal_handler.shutdown_event
+    episode_active = Event()
+
+    # Initialize Robot
+    follower_config = OpenArmsFollowerConfig(
+        port_left=cfg.follower_left_port,
+        port_right=cfg.follower_right_port,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=DEFAULT_CAMERA_CONFIG,
+    )
+
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+
+    if not follower.is_connected:
+        raise RuntimeError("Follower robot failed to connect!")
+
+    robot = RobotWrapper(follower)
+    logger.info("Follower robot connected")
+
+    # Build Processors and Dataset Features
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
+    action_features_hw = {}
+    for key, value in follower.action_features.items():
+        if key.endswith(".pos"):
+            action_features_hw[key] = value
+
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+
+    # Create or Load Dataset
+    dataset = None
+    dataset_lock = Lock()
+
+    if cfg.record_dataset:
+        dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / cfg.eval_dataset_id
+        if dataset_path.exists():
+            logger.info(f"Evaluation dataset exists at: {dataset_path}")
+            logger.info("New episodes will be appended.")
+            choice = input("Continue? (y/n): ").strip().lower()
+            if choice != "y":
+                logger.info("Aborting evaluation.")
+                follower.disconnect()
+                return
+
+        dataset = LeRobotDataset.create(
+            repo_id=cfg.eval_dataset_id,
+            fps=int(cfg.fps),
+            features=dataset_features,
+            robot_type=follower.name,
+            use_videos=True,
+            image_writer_processes=0,
+            image_writer_threads=12,
+        )
+        logger.info(f"Dataset created: {cfg.eval_dataset_id}")
+
+    # Load Policy
+    logger.info(f"Loading policy from: {cfg.model_id}")
+
+    policy_class = get_policy_class(cfg.policy.type)
+    config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
+
+    if cfg.policy.type in ["pi05", "pi0"]:
+        config.compile_model = cfg.use_torch_compile
+
+    policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
+
+    policy.config.rtc_config = cfg.rtc
+    policy.init_rtc_processor()
+
+    assert policy.name in ["smolvla", "pi05", "pi0"], "Only smolvla, pi05, and pi0 are supported for RTC"
+
+    policy = policy.to(cfg.device)
+    policy.eval()
+
+    if cfg.use_torch_compile:
+        policy = _apply_torch_compile(policy, cfg)
+
+    logger.info(f"Policy loaded: {policy.name}")
+
+    # Create Action Queue and Start Threads
+    action_queue = ActionQueue(cfg.rtc)
+
+    get_actions_t = Thread(
+        target=get_actions_thread,
+        args=(
+            policy,
+            robot,
+            robot_observation_processor,
+            action_queue,
+            shutdown_event,
+            cfg,
+            episode_active,
+        ),
+        daemon=True,
+        name="GetActions",
+    )
+    get_actions_t.start()
+    logger.info("Started action generation thread")
+
+    actor_t = Thread(
+        target=actor_thread,
+        args=(
+            robot,
+            robot_action_processor,
+            action_queue,
+            shutdown_event,
+            cfg,
+            episode_active,
+            dataset,
+            dataset_lock,
+            teleop_action_processor,
+            robot_observation_processor,
+        ),
+        daemon=True,
+        name="Actor",
+    )
+    actor_t.start()
+    logger.info("Started actor thread")
+
+    # Run Evaluation Episodes
+    episode_idx = 0
+
+    try:
+        while episode_idx < cfg.num_episodes and not shutdown_event.is_set():
+            log_say(f"Evaluating episode {episode_idx + 1} of {cfg.num_episodes}")
+            logger.info(f"\n{'='*40}")
+            logger.info(f"Episode {episode_idx + 1} / {cfg.num_episodes}")
+            logger.info(f"{'='*40}")
+
+            action_queue = ActionQueue(cfg.rtc)
+            episode_active.set()
+            episode_start_time = time.time()
+
+            while (time.time() - episode_start_time) < cfg.episode_time_sec:
+                if shutdown_event.is_set():
+                    break
+
+                elapsed = time.time() - episode_start_time
+                if int(elapsed) % 10 == 0 and int(elapsed) > 0:
+                    logger.info(
+                        f"[MAIN] Episode progress: {elapsed:.0f}/{cfg.episode_time_sec}s, "
+                        f"queue_size={action_queue.qsize()}"
+                    )
+
+                time.sleep(0.5)
+
+            episode_active.clear()
+            logger.info(f"Episode {episode_idx + 1} completed")
+
+            if cfg.record_dataset and dataset is not None:
+                with dataset_lock:
+                    if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                        logger.info(
+                            f"Saving episode {episode_idx + 1} "
+                            f"({dataset.episode_buffer['size']} frames)"
+                        )
+                        dataset.save_episode()
+
+            episode_idx += 1
+
+            # Manual reset between episodes
+            if not shutdown_event.is_set() and episode_idx < cfg.num_episodes:
+                log_say("Waiting for manual reset")
+                logger.info("Manually reset the environment and press ENTER to continue")
+                input("Press ENTER when ready...")
+
+        logger.info(f"Evaluation complete! {episode_idx} episodes recorded")
+        log_say("Evaluation complete", blocking=True)
+
+    except KeyboardInterrupt:
+        logger.info("\n\nEvaluation interrupted by user")
+
+    finally:
+        shutdown_event.set()
+        episode_active.clear()
+
+        if get_actions_t.is_alive():
+            logger.info("Waiting for action generation thread to finish...")
+            get_actions_t.join(timeout=5.0)
+
+        if actor_t.is_alive():
+            logger.info("Waiting for actor thread to finish...")
+            actor_t.join(timeout=5.0)
+
+        follower.disconnect()
+        logger.info("Follower disconnected")
+
+        if cfg.record_dataset and dataset is not None:
+            dataset.finalize()
+            if cfg.push_to_hub:
+                logger.info("Uploading to Hugging Face Hub...")
+                dataset.push_to_hub(private=True)
+
+        logger.info("Cleanup completed")
+
+
+if __name__ == "__main__":
+    main()

examples/openarms/friction_compensation.py

@@ -0,0 +1,216 @@
+import time
+import numpy as np
+
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+
+
+# Friction model parameters from OpenArms config/follower.yaml
+# τ_fric(ω) = Fo + Fv·ω + Fc·tanh(k·ω)
+# For 8 motors: [joint_1, joint_2, joint_3, joint_4, joint_5, joint_6, joint_7, gripper]
+FRICTION_PARAMS = {
+    "Fc": [0.306, 0.306, 0.40, 0.166, 0.050, 0.093, 0.172, 0.0512],  # Coulomb friction [Nm]
+    "k": [28.417, 28.417, 29.065, 130.038, 151.771, 242.287, 7.888, 4.000],  # tanh steepness
+    "Fv": [0.063, 0.0630, 0.604, 0.813, 0.029, 0.072, 0.084, 0.084],  # Viscous friction [Nm·s/rad]
+    "Fo": [0.088, 0.088, 0.008, -0.058, 0.005, 0.009, -0.059, -0.050],  # Offset torque [Nm]
+}
+
+# Constants from OpenArms C++ implementation
+AMP_TMP = 1.0
+COEF_TMP = 0.1
+
+FRICTION_SCALE = 1.0  # OpenArms C++ uses 0.3 factor in unilateral mode
+DAMPING_KD = [0.5, 0.5, 0.5, 0.5, 0.1, 0.1, 0.1, 0.1]  # Damping gains for stability
+
+def compute_friction_torque(velocity_rad_per_sec: float, motor_index: int) -> float:
+    """
+    Compute friction torque for a single motor using the tanh friction model.
+    
+    Args:
+        velocity_rad_per_sec: Angular velocity in rad/s
+        motor_index: Index of the motor (0-7)
+        
+    Returns:
+        Friction torque in N·m (scaled for stability)
+    """
+    
+    Fc = FRICTION_PARAMS["Fc"][motor_index]
+    k = FRICTION_PARAMS["k"][motor_index]
+    Fv = FRICTION_PARAMS["Fv"][motor_index]
+    Fo = FRICTION_PARAMS["Fo"][motor_index]
+    
+    # Friction model: τ_fric = amp * Fc * tanh(coef * k * ω) + Fv * ω + Fo
+    friction_torque = (
+        AMP_TMP * Fc * np.tanh(COEF_TMP * k * velocity_rad_per_sec) +
+        Fv * velocity_rad_per_sec +
+        Fo
+    )
+    
+    # Scale down friction compensation for stability at lower control rates
+    # (OpenArms C++ uses 0.3 factor in unilateral mode)!!
+    friction_torque *= FRICTION_SCALE
+    
+    return friction_torque
+
+
+def main() -> None:
+    config = OpenArmsFollowerConfig(
+        port_left="can0",
+        port_right="can1",
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=5.0,
+    )
+    
+    print("Initializing robot...")
+    follower = OpenArmsFollower(config)
+    follower.connect(calibrate=True)
+    
+    print(f"Applying friction compensation")
+    print("  1. Support the arm before starting")
+    print("  2. The arm will be held in place by friction compensation")
+    print("  3. You should be able to move it with gentle force")
+    print("\nPress ENTER when ready to start...")
+    input()
+    
+    print(f"✓ Motors enabled")
+    print("\nStarting friction compensation loop...")
+    print("Press Ctrl+C to stop\n")
+    
+    loop_times = []
+    last_print_time = time.perf_counter()
+    
+    # Motor name to index mapping
+    motor_name_to_index = {
+        "joint_1": 0,
+        "joint_2": 1,
+        "joint_3": 2,
+        "joint_4": 3,
+        "joint_5": 4,
+        "joint_6": 5,
+        "joint_7": 6,
+        "gripper": 7,
+    }
+    
+    try:
+        while True:
+            loop_start = time.perf_counter()
+            
+            # Get current joint positions and velocities from robot
+            obs = follower.get_observation()
+            
+            # Extract velocities in degrees per second
+            velocities_deg_per_sec = {}
+            positions_deg = {}
+            
+            for motor in follower.bus_right.motors:
+                vel_key = f"right_{motor}.vel"
+                pos_key = f"right_{motor}.pos"
+                if vel_key in obs:
+                    velocities_deg_per_sec[f"right_{motor}"] = obs[vel_key]
+                if pos_key in obs:
+                    positions_deg[f"right_{motor}"] = obs[pos_key]
+            
+            for motor in follower.bus_left.motors:
+                vel_key = f"left_{motor}.vel"
+                pos_key = f"left_{motor}.pos"
+                if vel_key in obs:
+                    velocities_deg_per_sec[f"left_{motor}"] = obs[vel_key]
+                if pos_key in obs:
+                    positions_deg[f"left_{motor}"] = obs[pos_key]
+            
+            # Convert velocities to rad/s and compute friction torques
+            friction_torques_nm = {}
+            for motor_full_name, velocity_deg_per_sec in velocities_deg_per_sec.items():
+                # Extract motor name without arm prefix
+                if motor_full_name.startswith("right_"):
+                    motor_name = motor_full_name.removeprefix("right_")
+                elif motor_full_name.startswith("left_"):
+                    motor_name = motor_full_name.removeprefix("left_")
+                else:
+                    continue
+                
+                # Get motor index for friction parameters
+                motor_index = motor_name_to_index.get(motor_name, 0)
+                
+                # Convert velocity to rad/s
+                velocity_rad_per_sec = np.deg2rad(velocity_deg_per_sec)
+                
+                # Compute friction torque
+                friction_torque = compute_friction_torque(velocity_rad_per_sec, motor_index)
+                friction_torques_nm[motor_full_name] = friction_torque
+            
+            # Apply friction compensation to right arm (all joints INCLUDING gripper)
+            for motor in follower.bus_right.motors:
+                full_name = f"right_{motor}"
+                position = positions_deg.get(full_name, 0.0)
+                torque = friction_torques_nm.get(full_name, 0.0)
+                
+                # Get motor index for damping gain
+                motor_index = motor_name_to_index.get(motor, 0)
+                kd = DAMPING_KD[motor_index]
+                
+                # Send MIT control command with friction compensation + damping
+                follower.bus_right._mit_control(
+                    motor=motor,
+                    kp=0.0,  # No position control
+                    kd=kd,   # Add damping for stability
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque
+                )
+
+            # Apply friction compensation to left arm (all joints INCLUDING gripper)
+            for motor in follower.bus_left.motors:
+                full_name = f"left_{motor}"
+                position = positions_deg.get(full_name, 0.0)
+                torque = friction_torques_nm.get(full_name, 0.0)
+                
+                # Get motor index for damping gain
+                motor_index = motor_name_to_index.get(motor, 0)
+                kd = DAMPING_KD[motor_index]
+                
+                # Send MIT control command with friction compensation + damping
+                follower.bus_left._mit_control(
+                    motor=motor,
+                    kp=0.0,  # No position control
+                    kd=kd,   # Add damping for stability
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque
+                )
+            
+            # Measure loop time
+            loop_end = time.perf_counter()
+            loop_time = loop_end - loop_start
+            loop_times.append(loop_time)
+            
+            # Print status every 2 seconds
+            if loop_end - last_print_time >= 2.0:
+                if loop_times:
+                    avg_time = sum(loop_times) / len(loop_times)
+                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
+                    
+                    print(f"{current_hz:.1f} Hz")
+
+                    loop_times = []
+                    last_print_time = loop_end
+            
+            time.sleep(0.001)
+            
+    except KeyboardInterrupt:
+        print("\n\nStopping friction compensation...")
+    
+    finally:
+        print("\nDisabling all motors and disconnecting...")
+        follower.bus_right.disable_torque()
+        follower.bus_left.disable_torque()
+        time.sleep(0.1)
+        follower.disconnect()
+        print("✓ Safe shutdown complete")
+
+
+if __name__ == "__main__":
+    main()
+

examples/openarms/gravity_compensation.py

@@ -0,0 +1,142 @@
+import time
+import numpy as np
+import pinocchio as pin
+from os.path import join, dirname, exists, expanduser
+
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+
+
+def main() -> None:
+    config = OpenArmsFollowerConfig(
+        port_left="can0",
+        port_right="can1",
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=5.0,
+    )
+    
+    
+    print("Initializing robot...")
+    follower = OpenArmsFollower(config)
+    follower.connect(calibrate=True)
+    
+    # Load URDF for Pinocchio dynamics
+    urdf_path = "/home/croissant/Documents/openarm_description/openarm_bimanual_pybullet.urdf"
+    
+    pin_robot = pin.RobotWrapper.BuildFromURDF(urdf_path, dirname(urdf_path))
+    pin_robot.data = pin_robot.model.createData()
+    print(f"✓ Loaded Pinocchio model with {pin_robot.nq} DoFs")
+    
+    follower.pin_robot = pin_robot
+    
+    print(f"Applying gravity compensation")
+    print("  1. Support the arm before starting")
+    print("  2. The arm will be held in place by gravity compensation")
+    print("  3. You should be able to move it with gentle force")
+    print("\nPress ENTER when ready to start...")
+    input()
+    
+    print(f"✓ Motors enabled")
+    print("\nStarting gravity compensation loop...")
+    print("Press Ctrl+C to stop\n")
+    
+    loop_times = []
+    last_print_time = time.perf_counter()
+    
+    try:
+        while True:
+            loop_start = time.perf_counter()
+            
+            # Get current joint positions from robot
+            obs = follower.get_observation()
+            
+            # Extract positions in degrees
+            positions_deg = {}
+            for motor in follower.bus_right.motors:
+                key = f"right_{motor}.pos"
+                if key in obs:
+                    positions_deg[f"right_{motor}"] = obs[key]
+            
+            for motor in follower.bus_left.motors:
+                key = f"left_{motor}.pos"
+                if key in obs:
+                    positions_deg[f"left_{motor}"] = obs[key]
+            
+            # Convert to radians and calculate gravity torques
+            # Use the built-in method from OpenArmsFollower
+            positions_rad = {k: np.deg2rad(v) for k, v in positions_deg.items()}
+            torques_nm = follower._gravity_from_q(positions_rad)
+            
+            # Apply gravity compensation to right arm (all joints except gripper)
+            for motor in follower.bus_right.motors:
+                if motor == "gripper":
+                    continue  # Skip gripper
+                    
+                full_name = f"right_{motor}"
+                position = positions_deg.get(full_name, 0.0)
+                torque = torques_nm.get(full_name, 0.0)
+                
+                # Send MIT control command with gravity compensation torque
+                follower.bus_right._mit_control(
+                    motor=motor,
+                    kp=0.0,  # No position control
+                    kd=0.0,  # No velocity damping
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque
+                )
+
+            # Apply gravity compensation to left arm (all joints except gripper)
+            for motor in follower.bus_left.motors:
+                if motor == "gripper":
+                    continue  # Skip gripper
+                    
+                full_name = f"left_{motor}"
+                position = positions_deg.get(full_name, 0.0)
+                torque = torques_nm.get(full_name, 0.0)
+                
+                # Send MIT control command with gravity compensation torque
+                follower.bus_left._mit_control(
+                    motor=motor,
+                    kp=0.0,  # No position control
+                    kd=0.0,  # No velocity damping
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque
+                )
+            
+            # Measure loop time
+            loop_end = time.perf_counter()
+            loop_time = loop_end - loop_start
+            loop_times.append(loop_time)
+            
+            # Print status every 2 seconds
+            if loop_end - last_print_time >= 2.0:
+                if loop_times:
+                    avg_time = sum(loop_times) / len(loop_times)
+                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
+                    
+                    print(f"{current_hz:.1f} Hz ({avg_time*1000:.1f} ms)")
+
+                    loop_times = []
+                    last_print_time = loop_end
+            
+            time.sleep(0.005)
+            
+    except KeyboardInterrupt:
+        print("\n\nStopping gravity compensation...")
+    
+    finally:
+        print("\nDisabling all motors and disconnecting...")
+        follower.bus_right.disable_torque()
+        follower.bus_left.disable_torque()
+        time.sleep(0.1)
+        follower.disconnect()
+        print("✓ Safe shutdown complete")
+
+
+if __name__ == "__main__":
+    main()
+

examples/openarms/record_with_compensation.py

@@ -0,0 +1,395 @@
+"""
+OpenArms Dataset Recording with Gravity + Friction Compensation
+
+Records a dataset using OpenArms follower robot with leader teleoperator.
+Leader arms have gravity and friction compensation for weightless, easy movement.
+Includes 3 cameras: left wrist, right wrist, and base camera.
+
+Uses the same compensation approach as teleop_with_compensation.py
+"""
+
+import shutil
+import time
+from pathlib import Path
+
+import numpy as np
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+
+# Recording parameters
+NUM_EPISODES = 1
+FPS = 30
+EPISODE_TIME_SEC = 600
+RESET_TIME_SEC = 120
+TASK_DESCRIPTION = "OpenArms task description"
+
+# Friction compensation scale factor (1.0 = full, 0.3 = 30% for stability)
+FRICTION_SCALE = 1.0
+
+def record_loop_with_compensation(
+    robot,
+    leader,
+    events,
+    fps,
+    dataset,
+    dataset_features,
+    control_time_s,
+    single_task,
+    display_data=True,
+):
+    """
+    Custom record loop that applies gravity + friction compensation to leader.
+    Based on record_loop but with integrated compensation.
+    """
+    dt = 1 / fps
+    episode_start_time = time.perf_counter()
+    
+    # All joints (both arms)
+    all_joints = []
+    for motor in leader.bus_right.motors:
+        all_joints.append(f"right_{motor}")
+    for motor in leader.bus_left.motors:
+        all_joints.append(f"left_{motor}")
+    
+    while True:
+        loop_start = time.perf_counter()
+        elapsed = loop_start - episode_start_time
+        
+        # Check if we should exit
+        if elapsed >= control_time_s or events["exit_early"] or events["stop_recording"]:
+            break
+        
+        # Get leader state
+        leader_action = leader.get_action()
+        
+        # Extract positions and velocities in degrees
+        leader_positions_deg = {}
+        leader_velocities_deg_per_sec = {}
+        
+        for motor in leader.bus_right.motors:
+            pos_key = f"right_{motor}.pos"
+            vel_key = f"right_{motor}.vel"
+            if pos_key in leader_action:
+                leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
+            if vel_key in leader_action:
+                leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
+        
+        for motor in leader.bus_left.motors:
+            pos_key = f"left_{motor}.pos"
+            vel_key = f"left_{motor}.vel"
+            if pos_key in leader_action:
+                leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
+            if vel_key in leader_action:
+                leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
+        
+        # Calculate gravity torques for leader using built-in method
+        leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
+        leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
+        
+        # Calculate friction torques for leader using built-in method
+        leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
+        leader_friction_torques_nm = leader._friction_from_velocity(
+            leader_velocities_rad_per_sec,
+            friction_scale=FRICTION_SCALE
+        )
+        
+        # Combine gravity + friction torques
+        leader_total_torques_nm = {}
+        for motor_name in leader_gravity_torques_nm:
+            gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
+            friction = leader_friction_torques_nm.get(motor_name, 0.0)
+            leader_total_torques_nm[motor_name] = gravity + friction
+        
+        # Apply gravity + friction compensation to leader RIGHT arm (all joints including gripper)
+        for motor in leader.bus_right.motors:
+            full_name = f"right_{motor}"
+            position = leader_positions_deg.get(full_name, 0.0)
+            torque = leader_total_torques_nm.get(full_name, 0.0)
+            
+            # Get damping gain for stability
+            kd = leader.get_damping_kd(motor)
+            
+            leader.bus_right._mit_control(
+                motor=motor,
+                kp=0.0,
+                kd=kd,  # Add damping for stability
+                position_degrees=position,
+                velocity_deg_per_sec=0.0,
+                torque=torque,
+            )
+        
+        # Apply gravity + friction compensation to leader LEFT arm (all joints including gripper)
+        for motor in leader.bus_left.motors:
+            full_name = f"left_{motor}"
+            position = leader_positions_deg.get(full_name, 0.0)
+            torque = leader_total_torques_nm.get(full_name, 0.0)
+            
+            # Get damping gain for stability
+            kd = leader.get_damping_kd(motor)
+            
+            leader.bus_left._mit_control(
+                motor=motor,
+                kp=0.0,
+                kd=kd,  # Add damping for stability
+                position_degrees=position,
+                velocity_deg_per_sec=0.0,
+                torque=torque,
+            )
+        
+        # Send leader positions to follower (both arms)
+        follower_action = {}
+        for joint in all_joints:
+            pos_key = f"{joint}.pos"
+            if pos_key in leader_action:
+                follower_action[pos_key] = leader_action[pos_key]
+        
+        # Send action to robot
+        if follower_action:
+            robot.send_action(follower_action)
+        
+        # Get observation from robot (includes camera images)
+        observation = robot.get_observation()
+        
+        # Add to dataset if we have a dataset
+        if dataset is not None:
+            # Build properly formatted observation frame
+            obs_frame = build_dataset_frame(dataset_features, observation, prefix="observation")
+            
+            # Build properly formatted action frame (keep .pos suffix - it matches the feature names)
+            action_frame = build_dataset_frame(dataset_features, follower_action, prefix="action")
+            
+            # Combine into single frame
+            frame = {**obs_frame, **action_frame}
+            
+            # Add metadata (task is required, timestamp will be auto-calculated by add_frame)
+            frame["task"] = single_task
+            
+            dataset.add_frame(frame)
+        
+        # Display data if requested
+        if display_data:
+            log_rerun_data(observation=observation, action=follower_action)
+        
+        # Maintain loop rate
+        loop_duration = time.perf_counter() - loop_start
+        sleep_time = dt - loop_duration
+        if sleep_time > 0:
+            time.sleep(sleep_time)
+
+
+def main():
+    """Main recording loop with gravity compensation."""
+    
+    print("=" * 70)
+    print("OpenArms Dataset Recording with Compensation")
+    print("=" * 70)
+    
+    # Create camera configurations (3 cameras: left wrist, right wrist, base)
+    # Using actual device paths found by lerobot-find-cameras opencv
+    camera_config = {
+        "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video0", width=640, height=480, fps=FPS),
+        "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
+        "base": OpenCVCameraConfig(index_or_path="/dev/video7", width=640, height=480, fps=FPS),
+    }
+    
+    # Configure follower robot with cameras
+    follower_config = OpenArmsFollowerConfig(
+        port_left="can2",
+        port_right="can3",
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=camera_config,
+    )
+    
+    # Configure leader teleoperator (no cameras needed)
+    leader_config = OpenArmsLeaderConfig(
+        port_left="can0",
+        port_right="can1",
+        can_interface="socketcan",
+        id="openarms_leader",
+        manual_control=False,  # Enable torque control for gravity compensation
+    )
+    
+    # Initialize robot and teleoperator
+    print("\nInitializing devices...")
+    follower = OpenArmsFollower(follower_config)
+    leader = OpenArmsLeader(leader_config)
+    
+    # Connect devices
+    print("Connecting and calibrating...")
+    follower.connect(calibrate=True)
+    leader.connect(calibrate=True)
+    
+    # Verify URDF is loaded for gravity compensation
+    if leader.pin_robot is None:
+        raise RuntimeError("URDF model not loaded on leader. Gravity compensation not available.")
+    
+    # Configure the dataset features
+    # For actions, we only want to record positions (not velocity or torque)
+    action_features_hw = {}
+    for key, value in follower.action_features.items():
+        if key.endswith(".pos"):
+            action_features_hw[key] = value
+    
+    action_features = hw_to_dataset_features(action_features_hw, "action")
+    obs_features = hw_to_dataset_features(follower.observation_features, "observation")
+    dataset_features = {**action_features, **obs_features}
+    
+    # Create the dataset
+    print("\nCreating dataset...")
+    repo_id = "<hf_username>/<dataset_repo_id>"  # TODO: Replace with your Hugging Face repo
+    
+    # Check if dataset already exists and prompt user
+    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / repo_id
+    while dataset_path.exists():
+        print(f"\nDataset already exists at: {dataset_path}")
+        print("\nOptions:")
+        print("  1. Overwrite existing dataset")
+        print("  2. Use a different name")
+        print("  3. Abort")
+        
+        choice = input("\nEnter your choice (1/2/3): ").strip()
+        
+        if choice == '1':
+            print(f"Removing existing dataset...")
+            shutil.rmtree(dataset_path)
+            print("✓ Existing dataset removed")
+            break
+        elif choice == '2':
+            print("\nCurrent repo_id:", repo_id)
+            new_repo_id = input("Enter new repo_id (format: <username>/<dataset_name>): ").strip()
+            if new_repo_id and '/' in new_repo_id:
+                repo_id = new_repo_id
+                dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / repo_id
+                print(f"✓ Using new repo_id: {repo_id}")
+                # Loop will continue if this new path also exists
+            else:
+                print("Invalid repo_id format. Please use format: <username>/<dataset_name>")
+        elif choice == '3':
+            print("Aborting. Please remove the existing dataset manually or restart with a different repo_id.")
+            follower.disconnect()
+            leader.disconnect()
+            return
+        else:
+            print("Invalid choice. Please enter 1, 2, or 3.")
+    
+    dataset = LeRobotDataset.create(
+        repo_id=repo_id,
+        fps=FPS,
+        features=dataset_features,
+        robot_type=follower.name,
+        use_videos=True,
+        image_writer_threads=4,
+    )
+    
+    # Initialize keyboard listener and visualization
+    _, events = init_keyboard_listener()
+    init_rerun(session_name="openarms_recording")
+    
+    # Enable motors on both leader arms for gravity compensation
+    leader.bus_right.enable_torque()
+    leader.bus_left.enable_torque()
+    time.sleep(0.1)
+    
+    print("\n" + "=" * 70)
+    print(f"Recording {NUM_EPISODES} episodes")
+    print(f"Task: {TASK_DESCRIPTION}")
+    print("=" * 70)
+    print("\nLeader BOTH arms: Gravity + Friction comp | Follower BOTH arms: Teleop")
+    print("\nKeyboard controls:")
+    print("  - Press 'q' to stop recording")
+    print("  - Press 'r' to re-record current episode")
+    print("=" * 70)
+    
+    episode_idx = 0
+    
+    try:
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+            
+            # Record episode with compensation active
+            record_loop_with_compensation(
+                robot=follower,
+                leader=leader,
+                events=events,
+                fps=FPS,
+                dataset=dataset,
+                dataset_features=dataset_features,
+                control_time_s=EPISODE_TIME_SEC,
+                single_task=TASK_DESCRIPTION,
+                display_data=True,
+            )
+            
+            # 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_with_compensation(
+                    robot=follower,
+                    leader=leader,
+                    events=events,
+                    fps=FPS,
+                    dataset=None,  # Don't save reset period
+                    dataset_features=dataset_features,
+                    control_time_s=RESET_TIME_SEC,
+                    single_task=TASK_DESCRIPTION,
+                    display_data=True,
+                )
+            
+            # Handle re-recording
+            if events["rerecord_episode"]:
+                log_say("Re-recording episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
+            
+            # Only save episode if frames were recorded
+            if dataset.episode_buffer is not None and dataset.episode_buffer["size"] > 0:
+                dataset.save_episode()
+                episode_idx += 1
+            else:
+                log_say("No frames recorded, skipping episode save")
+                # Clear the empty buffer
+                dataset.episode_buffer = None
+    
+    except KeyboardInterrupt:
+        print("\n\nStopping recording...")
+    
+    finally:
+        # Clean up
+        log_say("Stop recording")
+        try:
+            leader.bus_right.disable_torque()
+            leader.bus_left.disable_torque()
+            time.sleep(0.1)
+            leader.disconnect()
+            follower.disconnect()
+            print("✓ Shutdown complete")
+        except Exception as e:
+            print(f"Shutdown error: {e}")
+        
+        # Upload dataset
+        print("\nUploading dataset to Hugging Face Hub...")
+        try:
+            dataset.push_to_hub()
+            print("✓ Dataset uploaded successfully")
+        except Exception as e:
+            print(f"Warning: Failed to upload dataset: {e}")
+            print("You can manually upload later using: dataset.push_to_hub()")
+        
+        print("✓ Recording complete!")
+
+
+if __name__ == "__main__":
+    main()

examples/openarms/replay.py

@@ -0,0 +1,166 @@
+#!/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.
+
+"""
+OpenArms Dataset Replay Example
+
+Replays position actions from a recorded dataset on an OpenArms follower robot.
+Only position commands (ending with .pos) are replayed, not velocity or torque.
+
+Example usage:
+    python examples/openarms/replay.py
+"""
+
+import time
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.utils.constants import ACTION
+from lerobot.utils.robot_utils import busy_wait
+from lerobot.utils.utils import log_say
+
+# Configuration
+EPISODE_IDX = 0
+DATASET_REPO_ID = "lerobot-data-collection/replay-this-2025-11-02-17-58"  # TODO: Replace with your dataset
+DATASET_ROOT = None  # Use default cache location, or specify custom path
+
+# Robot configuration - adjust these to match your setup
+ROBOT_CONFIG = OpenArmsFollowerConfig(
+    port_left="can2",      # CAN interface for left arm
+    port_right="can3",     # CAN interface for right arm
+    can_interface="socketcan", 
+    id="openarms_follower",
+    disable_torque_on_disconnect=True,
+    max_relative_target=10.0,  # Safety limit: max degrees to move per step
+)
+
+
+def main():
+    """Main replay function."""
+    print("=" * 70)
+    print("OpenArms Dataset Replay")
+    print("=" * 70)
+    print(f"\nDataset: {DATASET_REPO_ID}")
+    print(f"Episode: {EPISODE_IDX}")
+    print(f"Robot: {ROBOT_CONFIG.id}")
+    print(f"  Left arm: {ROBOT_CONFIG.port_left}")
+    print(f"  Right arm: {ROBOT_CONFIG.port_right}")
+    print("\n" + "=" * 70)
+    
+    # Initialize the robot
+    print("\n[1/3] Initializing robot...")
+    robot = OpenArmsFollower(ROBOT_CONFIG)
+    
+    # Load the dataset
+    print(f"\n[2/3] Loading dataset '{DATASET_REPO_ID}'...")
+    dataset = LeRobotDataset(
+        DATASET_REPO_ID,
+        root=DATASET_ROOT,
+        episodes=[EPISODE_IDX]
+    )
+    
+    # Filter dataset to only include frames from the specified episode
+    # (required for dataset V3.0 where episodes are chunked)
+    episode_frames = dataset.hf_dataset.filter(
+        lambda x: x["episode_index"] == EPISODE_IDX
+    )
+    
+    if len(episode_frames) == 0:
+        raise ValueError(
+            f"No frames found for episode {EPISODE_IDX} in dataset {DATASET_REPO_ID}"
+        )
+    
+    print(f"  Found {len(episode_frames)} frames in episode {EPISODE_IDX}")
+    
+    # Extract action features from dataset
+    action_features = dataset.features.get(ACTION, {})
+    action_names = action_features.get("names", [])
+    
+    # Filter to only position actions (ending with .pos)
+    position_action_names = [name for name in action_names if name.endswith(".pos")]
+    
+    if not position_action_names:
+        raise ValueError(
+            f"No position actions found in dataset. Action names: {action_names}"
+        )
+    
+    print(f"  Found {len(position_action_names)} position actions to replay")
+    print(f"  Actions: {', '.join(position_action_names[:5])}{'...' if len(position_action_names) > 5 else ''}")
+    
+    # Select only action columns from dataset
+    actions = episode_frames.select_columns(ACTION)
+    
+    # Connect to the robot
+    print(f"\n[3/3] Connecting to robot...")
+    robot.connect(calibrate=False)  # Skip calibration for replay
+    
+    if not robot.is_connected:
+        raise RuntimeError("Robot failed to connect!")
+    
+    print("\n" + "=" * 70)
+    print("Ready to replay!")
+    print("=" * 70)
+    print("\nThe robot will replay the recorded positions.")
+    print("Press Ctrl+C to stop at any time.\n")
+    
+    input("Press ENTER to start replaying...")
+    
+    # Replay loop
+    log_say(f"Replaying episode {EPISODE_IDX}", blocking=True)
+    
+    try:
+        for idx in range(len(episode_frames)):
+            loop_start = time.perf_counter()
+            
+            # Extract action array from dataset
+            action_array = actions[idx][ACTION]
+            
+            # Build action dictionary, but only include position actions
+            action = {}
+            for i, name in enumerate(action_names):
+                # Only include position actions (ending with .pos)
+                if name.endswith(".pos"):
+                    action[name] = float(action_array[i])
+            
+            # Send action to robot
+            robot.send_action(action)
+            
+            # Maintain replay rate (use dataset fps)
+            loop_duration = time.perf_counter() - loop_start
+            dt_s = 1.0 / dataset.fps - loop_duration
+            busy_wait(dt_s)
+            
+            # Progress indicator every 100 frames
+            if (idx + 1) % 100 == 0:
+                progress = (idx + 1) / len(episode_frames) * 100
+                print(f"Progress: {idx + 1}/{len(episode_frames)} frames ({progress:.1f}%)")
+        
+        print(f"\n✓ Successfully replayed {len(episode_frames)} frames")
+        log_say("Replay complete", blocking=True)
+        
+    except KeyboardInterrupt:
+        print("\n\nReplay interrupted by user")
+    finally:
+        # Disconnect robot
+        print("\nDisconnecting robot...")
+        robot.disconnect()
+        print("✓ Replay complete!")
+
+
+if __name__ == "__main__":
+    main()
+

examples/openarms/setup_can.sh

@@ -0,0 +1,73 @@
+#!/bin/bash
+# Setup all OpenArms CAN interfaces with CAN FD
+
+set -e
+
+echo "=========================================="
+echo "OpenArms CAN FD Interface Setup"
+echo "=========================================="
+echo ""
+echo "Mode: CAN FD"
+echo "  - Nominal bitrate: 1 Mbps"
+echo "  - Data bitrate: 5 Mbps"
+echo ""
+echo "Configuring interfaces can0, can1, can2, can3..."
+echo ""
+
+# Configure each CAN interface with CAN FD
+for i in 0 1 2 3; do
+    interface="can$i"
+    
+    # Check if interface exists
+    if ! ip link show "$interface" &> /dev/null; then
+        echo "⚠ $interface: Not found, skipping"
+        continue
+    fi
+    
+    # Bring down interface
+    sudo ip link set "$interface" down 2>/dev/null
+    
+    # Configure CAN FD mode
+    sudo ip link set "$interface" type can \
+        bitrate 1000000 \
+        dbitrate 5000000 \
+        fd on
+    
+    # Bring up interface
+    sudo ip link set "$interface" up
+    
+    # Verify configuration
+    if ip link show "$interface" | grep -q "UP"; then
+        echo "✓ $interface: Configured and UP"
+    else
+        echo "✗ $interface: Failed to bring UP"
+    fi
+done
+
+echo ""
+echo "=========================================="
+echo "Verification"
+echo "=========================================="
+echo ""
+
+# Show detailed status for each interface
+for i in 0 1 2 3; do
+    interface="can$i"
+    if ip link show "$interface" &> /dev/null; then
+        echo "$interface:"
+        # Show key parameters
+        ip -d link show "$interface" | grep -E "can|state|bitrate|dbitrate" | head -3
+        echo ""
+    fi
+done
+
+echo "=========================================="
+echo "Setup Complete!"
+echo "=========================================="
+echo ""
+echo "All interfaces configured for CAN FD mode"
+echo ""
+echo "Next steps:"
+echo "  1. Test motors: python debug_can_communication.py"
+echo "  2. Run teleoperation: python examples/openarms/teleop.py"
+echo ""

examples/openarms/teleop.py

@@ -0,0 +1,148 @@
+"""
+OpenArms Teleoperation Example - Full Dual Arms
+
+This script demonstrates teleoperation of OpenArms follower robot using an OpenArms leader arm.
+It first calibrates both devices, then enters a teleoperation loop for both arms.
+"""
+
+import time
+
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+
+
+follower_config = OpenArmsFollowerConfig(
+    port_left="can2",   # CAN interface for follower left arm
+    port_right="can3",  # CAN interface for follower right arm
+    can_interface="socketcan",  # Linux SocketCAN
+    id="openarms_follower",
+    disable_torque_on_disconnect=True,
+    max_relative_target=5.0,  # Safety limit
+)
+
+
+leader_config = OpenArmsLeaderConfig(
+    port_left="can0",   # CAN interface for leader left arm
+    port_right="can1",  # CAN interface for leader right arm
+    can_interface="socketcan",  # Linux SocketCAN
+    id="openarms_leader",
+    manual_control=True,  # Enable manual control (torque disabled)
+)
+
+print("=" * 60)
+print("OpenArms Teleoperation - Full Dual Arms")
+print("=" * 60)
+
+# Initialize devices
+print("\n[1/4] Initializing devices...")
+follower = OpenArmsFollower(follower_config)
+leader = OpenArmsLeader(leader_config)
+
+# Connect and calibrate follower
+print("\n[2/4] Connecting and calibrating follower robot...")
+print("Note: If you have existing calibration, just press ENTER to use it.")
+follower.connect(calibrate=True)
+
+# Connect and calibrate leader
+print("\n[3/4] Connecting and calibrating leader arm...")
+print("Note: The leader arm will have torque disabled for manual control.")
+leader.connect(calibrate=True)
+
+# Wait for user to be ready
+print("\n[4/4] Ready for teleoperation!")
+print("\nBoth arms will be controlled (16 motors total):")
+print("  RIGHT ARM: joints 1-7 + gripper")
+print("  LEFT ARM: joints 1-7 + gripper")
+
+print("\nPress ENTER to start teleoperation...")
+input()
+
+print("\nTeleoperation started! Move both leader arms.")
+print("Press Ctrl+C to stop.\n")
+
+# All joints for both arms (16 motors total)
+all_joints = [
+    # Right arm
+    "right_joint_1",
+    "right_joint_2",
+    "right_joint_3",
+    "right_joint_4",
+    "right_joint_5",
+    "right_joint_6",
+    "right_joint_7",
+    "right_gripper",
+    # Left arm
+    "left_joint_1",
+    "left_joint_2",
+    "left_joint_3",
+    "left_joint_4",
+    "left_joint_5",
+    "left_joint_6",
+    "left_joint_7",
+    "left_gripper",
+]
+
+# Performance monitoring
+loop_times = []
+start_time = time.perf_counter()
+last_print_time = start_time
+
+try:
+    while True:
+        loop_start = time.perf_counter()
+        
+        # Get action from leader
+        leader_action = leader.get_action()
+        
+        # Filter to only position data for all joints (both arms)
+        joint_action = {}
+        for joint in all_joints:
+            pos_key = f"{joint}.pos"
+            if pos_key in leader_action:
+                joint_action[pos_key] = leader_action[pos_key]
+        
+        # Send action to follower (both arms)
+        if joint_action:
+            follower.send_action(joint_action)
+        
+        # Measure loop time
+        loop_end = time.perf_counter()
+        loop_time = loop_end - loop_start
+        loop_times.append(loop_time)
+        
+        # Print stats every 2 seconds
+        if loop_end - last_print_time >= 2.0:
+            if loop_times:
+                avg_time = sum(loop_times) / len(loop_times)
+                current_hz = 1.0 / avg_time if avg_time > 0 else 0
+                min_time = min(loop_times)
+                max_time = max(loop_times)
+                max_hz = 1.0 / min_time if min_time > 0 else 0
+                min_hz = 1.0 / max_time if max_time > 0 else 0
+                
+                print(f"[Hz Stats] Avg: {current_hz:.1f} Hz | "
+                      f"Range: {min_hz:.1f}-{max_hz:.1f} Hz | "
+                      f"Avg loop time: {avg_time*1000:.1f} ms")
+                
+                # Reset for next measurement window
+                loop_times = []
+                last_print_time = loop_end
+            
+except KeyboardInterrupt:
+    print("\n\nStopping teleoperation...")
+finally:
+    # Disconnect devices
+    print("Disconnecting devices...")
+    try:
+        follower.disconnect()
+    except Exception as e:
+        print(f"Error disconnecting follower: {e}")
+    
+    try:
+        leader.disconnect()
+    except Exception as e:
+        print(f"Error disconnecting leader: {e}")
+    
+    print("Done!")

examples/openarms/teleop_openarms_mini.py

@@ -0,0 +1,197 @@
+"""
+OpenArms Mini Teleoperation Example
+
+This script demonstrates teleoperation of an OpenArms follower robot using 
+an OpenArms Mini leader (Feetech-based) with dual arms (16 motors total).
+
+The OpenArms Mini has:
+- Right arm: 8 motors (joint_1 to joint_7 + gripper)
+- Left arm: 8 motors (joint_1 to joint_7 + gripper)
+
+Note on gripper normalization:
+- OpenArms Mini gripper: 0-100 scale (0=closed, 100=open)
+- OpenArms follower gripper: degrees (0=closed, -65=open)
+- This script automatically converts between the two ranges
+"""
+
+import time
+import os
+import sys
+
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.teleoperators.openarms_mini.openarms_mini import OpenArmsMini
+from lerobot.teleoperators.openarms_mini.config_openarms_mini import OpenArmsMiniConfig
+from lerobot.utils.robot_utils import busy_wait
+
+# Target control frequency
+TARGET_FPS = 30
+
+# Configure the OpenArms follower (Damiao motors on CAN bus)
+follower_config = OpenArmsFollowerConfig(
+    port_left="can0",   # CAN interface for follower left arm
+    port_right="can1",  # CAN interface for follower right arm
+    can_interface="socketcan",  # Linux SocketCAN
+    id="openarms_follower",
+    disable_torque_on_disconnect=True,
+    max_relative_target=10.0,  # Safety limit (degrees per step)
+)
+
+# Configure the OpenArms Mini leader (Feetech motors on serial)
+leader_config = OpenArmsMiniConfig(
+    port_right="/dev/ttyACM0",  # Serial port for right arm
+    port_left="/dev/ttyACM1",   # Serial port for left arm
+    id="openarms_mini",
+    use_degrees=True,
+)
+
+print("OpenArms Mini → OpenArms Follower Teleoperation")
+
+# Initialize devices
+follower = OpenArmsFollower(follower_config)
+leader = OpenArmsMini(leader_config)
+
+# Connect and calibrate follower
+print("Note: If you have existing calibration, just press ENTER to use it.")
+follower.connect(calibrate=True)
+
+# Connect and calibrate leader
+print("Note: The leader arms will have torque disabled for manual control.")
+leader.connect(calibrate=True)
+
+print("\nPress ENTER to start teleoperation...")
+input()
+
+print("Press Ctrl+C to stop.\n")
+
+# All joints for both arms (16 motors total)
+all_joints = [
+    # Right arm
+    "right_joint_1",
+    "right_joint_2",
+    "right_joint_3",
+    "right_joint_4",
+    "right_joint_5",
+    "right_joint_6",
+    "right_joint_7",
+    "right_gripper",
+    # Left arm
+    "left_joint_1",
+    "left_joint_2",
+    "left_joint_3",
+    "left_joint_4",
+    "left_joint_5",
+    "left_joint_6",
+    "left_joint_7",
+    "left_gripper",
+]
+
+# Performance monitoring
+loop_times = []
+avg_loop_time = 0.0
+min_loop_time = float('inf')
+max_loop_time = 0.0
+stats_update_interval = 1.0  # Update stats every 1 second
+last_stats_update = time.perf_counter()
+
+
+SWAPPED_JOINTS = {
+    "right_joint_6": "right_joint_7",
+    "right_joint_7": "right_joint_6",
+    "left_joint_6": "left_joint_7",
+    "left_joint_7": "left_joint_6",
+}
+
+try:
+    while True:
+        loop_start = time.perf_counter()
+
+        # Get actions and observations
+        leader_action = leader.get_action()
+        follower_obs = follower.get_observation()
+
+        joint_action = {}
+        for joint in all_joints:
+            leader_key = f"{joint}.pos"
+
+            # Determine which follower joint this leader joint controls
+            follower_joint = SWAPPED_JOINTS.get(joint, joint)
+            follower_key = f"{follower_joint}.pos"
+
+            # Get leader position (default 0 if missing)
+            pos = leader_action.get(leader_key, 0.0)
+
+            # Convert gripper values: Mini uses 0-100, OpenArms uses 0 to -65 degrees
+            if "gripper" in joint:
+                # Map 0-100 (Mini) to 0 to -65 (OpenArms)
+                # 0 (closed) -> 0°, 100 (open) -> -65°
+                pos = (pos / 100.0) * -65.0
+
+            # Store in action dict for follower
+            joint_action[follower_key] = pos
+
+        follower.send_action(joint_action)
+
+        # Loop timing
+        loop_end = time.perf_counter()
+        loop_time = loop_end - loop_start
+        loop_times.append(loop_time)
+
+        # Update stats periodically
+        current_time = time.perf_counter()
+        if current_time - last_stats_update >= stats_update_interval:
+            if loop_times:
+                avg_loop_time = sum(loop_times) / len(loop_times)
+                min_loop_time = min(loop_times)
+                max_loop_time = max(loop_times)
+                loop_times = []
+                last_stats_update = current_time
+
+        # Display everything
+        sys.stdout.write("\033[H\033[J")  # Clear screen
+        
+        # Show timing stats at the top
+        if avg_loop_time > 0:
+            avg_hz = 1.0 / avg_loop_time
+            min_hz = 1.0 / max_loop_time if max_loop_time > 0 else 0
+            max_hz = 1.0 / min_loop_time if min_loop_time > 0 and min_loop_time < float('inf') else 0
+            print(f"[Performance] Target: {TARGET_FPS} Hz | Avg: {avg_hz:.1f} Hz | Range: {min_hz:.1f}-{max_hz:.1f} Hz | Loop: {avg_loop_time*1000:.1f} ms\n")
+        else:
+            print(f"[Performance] Target: {TARGET_FPS} Hz | Measuring...\n")
+
+        # Show joint positions
+        print(f"{'Joint':<20} {'Leader':>15} {'Follower':>15}")
+        print(f"{'':20} {'(0-100/deg)':>15} {'(deg)':>15}")
+        print("-" * 52)
+
+        for joint in all_joints:
+            leader_key = f"{joint}.pos"
+            follower_joint = SWAPPED_JOINTS.get(joint, joint)
+            follower_key = f"{follower_joint}.pos"
+
+            leader_pos = leader_action.get(leader_key, 0.0)
+            follower_pos = follower_obs.get(follower_key, 0.0)
+
+            print(f"{joint:<20} {leader_pos:>15.2f} {follower_pos:>15.2f}")
+
+        # Smart sleep to maintain target FPS
+        dt_s = time.perf_counter() - loop_start
+        busy_wait(max(0, 1.0 / TARGET_FPS - dt_s))
+            
+except KeyboardInterrupt:
+    print("\n\nStopping teleoperation...")
+finally:
+    # Disconnect devices
+    print("Disconnecting devices...")
+    try:
+        follower.disconnect()
+    except Exception as e:
+        print(f"Error disconnecting follower: {e}")
+    
+    try:
+        leader.disconnect()
+    except Exception as e:
+        print(f"Error disconnecting leader: {e}")
+    
+    print("Done!")
+

examples/openarms/teleop_with_compensation.py

@@ -0,0 +1,202 @@
+"""
+OpenArms Teleoperation with Gravity + Friction Compensation
+
+Leader arms (both LEFT and RIGHT): Gravity + Friction compensation (weightless, easy to move)
+Follower arms (both LEFT and RIGHT): Mirror leader movements
+
+Uses the URDF file from the lerobot repository.
+"""
+
+import time
+
+import numpy as np
+
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.teleoperators.openarms.config_openarms_leader import OpenArmsLeaderConfig
+from lerobot.teleoperators.openarms.openarms_leader import OpenArmsLeader
+
+# Friction compensation scale factor (1.0 = full, 0.3 = 30% for stability)
+FRICTION_SCALE = 1.0
+
+def main():
+    """Main teleoperation loop with gravity compensation"""
+
+    print("=" * 70)
+    print("OpenArms Teleoperation with Gravity Compensation")
+    print("=" * 70)
+
+    # Configuration
+    follower_config = OpenArmsFollowerConfig(
+        port_left="can2",
+        port_right="can3",
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+    )
+
+    leader_config = OpenArmsLeaderConfig(
+        port_left="can0",
+        port_right="can1",
+        can_interface="socketcan",
+        id="openarms_leader",
+        manual_control=False,  # Enable torque control for gravity compensation
+    )
+
+    # Initialize and connect
+    print("\nInitializing devices...")
+    follower = OpenArmsFollower(follower_config)
+    leader = OpenArmsLeader(leader_config)
+
+    follower.connect()
+    leader.connect()
+
+    # URDF is automatically loaded in the leader constructor
+    if leader.pin_robot is None:
+        raise RuntimeError("URDF model not loaded on leader. Gravity compensation not available.")
+
+    print("\nLeader BOTH arms: Gravity + Friction comp | Follower BOTH arms: Teleop")
+    print("Press ENTER to start...")
+    input()
+
+    # Enable motors on both leader arms for gravity compensation
+    leader.bus_right.enable_torque()
+    leader.bus_left.enable_torque()
+    time.sleep(0.1)
+
+    print("Press Ctrl+C to stop\n")
+
+    # Main control loop
+    loop_times = []
+    last_print_time = time.perf_counter()
+
+    # All joints (both arms)
+    all_joints = []
+    for motor in leader.bus_right.motors:
+        all_joints.append(f"right_{motor}")
+    for motor in leader.bus_left.motors:
+        all_joints.append(f"left_{motor}")
+
+    try:
+        while True:
+            loop_start = time.perf_counter()
+
+            # Get leader state
+            leader_action = leader.get_action()
+
+            # Extract positions and velocities in degrees
+            leader_positions_deg = {}
+            leader_velocities_deg_per_sec = {}
+            
+            for motor in leader.bus_right.motors:
+                pos_key = f"right_{motor}.pos"
+                vel_key = f"right_{motor}.vel"
+                if pos_key in leader_action:
+                    leader_positions_deg[f"right_{motor}"] = leader_action[pos_key]
+                if vel_key in leader_action:
+                    leader_velocities_deg_per_sec[f"right_{motor}"] = leader_action[vel_key]
+
+            for motor in leader.bus_left.motors:
+                pos_key = f"left_{motor}.pos"
+                vel_key = f"left_{motor}.vel"
+                if pos_key in leader_action:
+                    leader_positions_deg[f"left_{motor}"] = leader_action[pos_key]
+                if vel_key in leader_action:
+                    leader_velocities_deg_per_sec[f"left_{motor}"] = leader_action[vel_key]
+
+            # Calculate gravity torques for leader using built-in method
+            leader_positions_rad = {k: np.deg2rad(v) for k, v in leader_positions_deg.items()}
+            leader_gravity_torques_nm = leader._gravity_from_q(leader_positions_rad)
+            
+            # Calculate friction torques for leader using built-in method
+            leader_velocities_rad_per_sec = {k: np.deg2rad(v) for k, v in leader_velocities_deg_per_sec.items()}
+            leader_friction_torques_nm = leader._friction_from_velocity(
+                leader_velocities_rad_per_sec,
+                friction_scale=FRICTION_SCALE
+            )
+            
+            # Combine gravity + friction torques 
+            leader_total_torques_nm = {}
+            for motor_name in leader_gravity_torques_nm:
+                gravity = leader_gravity_torques_nm.get(motor_name, 0.0)
+                friction = leader_friction_torques_nm.get(motor_name, 0.0)
+                leader_total_torques_nm[motor_name] = gravity + friction
+
+            # Apply gravity + friction compensation to leader RIGHT arm (all joints including gripper)
+            for motor in leader.bus_right.motors:
+                full_name = f"right_{motor}"
+                position = leader_positions_deg.get(full_name, 0.0)
+                torque = leader_total_torques_nm.get(full_name, 0.0)
+                
+                # Get damping gain for stability
+                kd = leader.get_damping_kd(motor)
+
+                leader.bus_right._mit_control(
+                    motor=motor,
+                    kp=0.0,
+                    kd=kd,  # Add damping for stability
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque,
+                )
+
+            # Apply gravity + friction compensation to leader LEFT arm (all joints including gripper)
+            for motor in leader.bus_left.motors:
+                full_name = f"left_{motor}"
+                position = leader_positions_deg.get(full_name, 0.0)
+                torque = leader_total_torques_nm.get(full_name, 0.0)
+                
+                # Get damping gain for stability
+                kd = leader.get_damping_kd(motor)
+
+                leader.bus_left._mit_control(                    
+                    motor=motor,
+                    kp=0.0,
+                    kd=kd,  # Add damping for stability
+                    position_degrees=position,
+                    velocity_deg_per_sec=0.0,
+                    torque=torque,
+                )
+
+            # Send leader positions to follower (both arms)
+            follower_action = {}
+            for joint in all_joints:
+                pos_key = f"{joint}.pos"
+                if pos_key in leader_action:
+                    follower_action[pos_key] = leader_action[pos_key]
+
+            if follower_action:
+                follower.send_action(follower_action)
+
+            # Performance monitoring
+            loop_end = time.perf_counter()
+            loop_time = loop_end - loop_start
+            loop_times.append(loop_time)
+
+            if loop_end - last_print_time >= 2.0:
+                if loop_times:
+                    avg_time = sum(loop_times) / len(loop_times)
+                    current_hz = 1.0 / avg_time if avg_time > 0 else 0
+
+                    print(f"{current_hz:.1f} Hz ({avg_time*1000:.1f} ms)")
+
+                    loop_times = []
+                    last_print_time = loop_end
+
+    except KeyboardInterrupt:
+        print("\n\nStopping...")
+    finally:
+        try:
+            leader.bus_right.disable_torque()
+            leader.bus_left.disable_torque()
+            time.sleep(0.1)
+            leader.disconnect()
+            follower.disconnect()
+            print("✓ Shutdown complete")
+        except Exception as e:
+            print(f"Shutdown error: {e}")
+
+
+if __name__ == "__main__":
+    main()

examples/openarms_web_interface/App.css

@@ -0,0 +1,745 @@
+body {
+  margin: 0;
+  padding: 0;
+  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif;
+  background: #f5f5f5;
+}
+
+main {
+  min-height: 100vh;
+  padding: 2rem;
+}
+
+header {
+  text-align: center;
+  margin-bottom: 2rem;
+}
+
+h1 {
+  font-size: 2rem;
+  font-weight: 600;
+  color: #333;
+  margin: 0;
+}
+
+h2 {
+  font-size: 1.25rem;
+  font-weight: 600;
+  color: #333;
+  margin: 0 0 1rem 0;
+}
+
+h3 {
+  font-size: 0.875rem;
+  font-weight: 600;
+  color: #666;
+  margin: 0 0 0.5rem 0;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+}
+
+.container {
+  max-width: 1920px;
+  margin: 0 auto;
+  display: grid;
+  grid-template-columns: minmax(500px, 600px) 1fr;
+  gap: 2rem;
+  align-items: start;
+}
+
+/* Left column container */
+.left-column {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+/* Right column container */
+.right-column {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+/* Responsive: Stack on smaller screens */
+@media (max-width: 1200px) {
+  .container {
+    grid-template-columns: 1fr;
+  }
+}
+
+.panel {
+  background: white;
+  border-radius: 8px;
+  padding: 1.5rem;
+  box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+}
+
+.config-panel {
+  border: 2px solid #e5e7eb;
+}
+
+.config-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  cursor: pointer;
+  user-select: none;
+  padding: 0.5rem 0;
+}
+
+.config-header:hover {
+  opacity: 0.7;
+}
+
+.toggle-icon {
+  font-size: 1rem;
+  color: #6b7280;
+  transition: transform 0.2s;
+}
+
+.config-content {
+  margin-top: 1rem;
+  padding-top: 1rem;
+  border-top: 1px solid #e5e7eb;
+}
+
+.robot-setup {
+  margin-bottom: 0.5rem;
+}
+
+.robot-status {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 1rem;
+  border-radius: 6px;
+  font-weight: 500;
+  gap: 1rem;
+}
+
+.robot-status.ready {
+  background: linear-gradient(135deg, #d1fae5 0%, #a7f3d0 100%);
+  color: #065f46;
+  border: 1px solid #10b981;
+}
+
+.robot-status.not-ready {
+  background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
+  color: #92400e;
+  border: 1px solid #f59e0b;
+}
+
+.btn-setup {
+  background: #10b981;
+  color: white;
+  border: none;
+  padding: 0.5rem 1rem;
+  border-radius: 4px;
+  font-size: 0.875rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.btn-setup:hover:not(:disabled) {
+  background: #059669;
+}
+
+.btn-setup:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.btn-zero {
+  background: #8b5cf6;
+  color: white;
+  border: none;
+  padding: 0.5rem 1rem;
+  border-radius: 4px;
+  font-size: 0.875rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.btn-zero:hover:not(:disabled) {
+  background: #7c3aed;
+}
+
+.btn-zero:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.zero-position-section {
+  margin-top: 1rem;
+  padding-top: 1rem;
+  border-top: 1px solid #e5e7eb;
+}
+
+.btn-zero-large {
+  width: 100%;
+  background: #8b5cf6;
+  color: white;
+  border: none;
+  padding: 0.875rem 1.5rem;
+  border-radius: 8px;
+  font-size: 1rem;
+  font-weight: 600;
+  cursor: pointer;
+  transition: all 0.2s;
+  box-shadow: 0 2px 4px rgba(139, 92, 246, 0.2);
+}
+
+.btn-zero-large:hover:not(:disabled) {
+  background: #7c3aed;
+  box-shadow: 0 4px 8px rgba(139, 92, 246, 0.3);
+  transform: translateY(-1px);
+}
+
+.btn-zero-large:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+  box-shadow: none;
+  transform: none;
+}
+
+.delete-episode-section {
+  margin-top: 1rem;
+  padding-top: 1rem;
+  border-top: 1px solid #e5e7eb;
+}
+
+.btn-delete {
+  width: 100%;
+  background: #ef4444;
+  color: white;
+  border: none;
+  padding: 0.875rem 1.5rem;
+  border-radius: 8px;
+  font-size: 1rem;
+  font-weight: 600;
+  cursor: pointer;
+  transition: all 0.2s;
+  box-shadow: 0 2px 4px rgba(239, 68, 68, 0.2);
+}
+
+.btn-delete:hover:not(:disabled) {
+  background: #dc2626;
+  box-shadow: 0 4px 8px rgba(239, 68, 68, 0.3);
+  transform: translateY(-1px);
+}
+
+.btn-delete:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+  box-shadow: none;
+  transform: none;
+}
+
+.delete-info {
+  margin-top: 0.5rem;
+  font-size: 0.875rem;
+  color: #666;
+  text-align: center;
+  font-style: italic;
+}
+
+.btn-disconnect {
+  background: #ef4444;
+  color: white;
+  border: none;
+  padding: 0.5rem 1rem;
+  border-radius: 4px;
+  font-size: 0.875rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.btn-disconnect:hover {
+  background: #dc2626;
+}
+
+.btn-refresh {
+  background: #3b82f6;
+  color: white;
+  border: none;
+  padding: 0.4rem 0.8rem;
+  border-radius: 4px;
+  font-size: 0.75rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.btn-refresh:hover:not(:disabled) {
+  background: #2563eb;
+}
+
+.btn-refresh:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.control-panel {
+  border: 2px solid #10b981;
+}
+
+.status-banner {
+  display: flex;
+  align-items: center;
+  gap: 1rem;
+  padding: 1rem 1.5rem;
+  border-radius: 6px;
+  margin-bottom: 1.5rem;
+  font-weight: 500;
+  font-size: 0.95rem;
+}
+
+.status-banner.initializing {
+  background: linear-gradient(135deg, #dbeafe 0%, #bfdbfe 100%);
+  color: #1e40af;
+  border-left: 4px solid #3b82f6;
+}
+
+.status-banner.encoding {
+  background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
+  color: #92400e;
+  border-left: 4px solid #f59e0b;
+}
+
+.status-banner.uploading {
+  background: linear-gradient(135deg, #e0e7ff 0%, #c7d2fe 100%);
+  color: #3730a3;
+  border-left: 4px solid #6366f1;
+}
+
+.status-banner.success {
+  background: linear-gradient(135deg, #d1fae5 0%, #a7f3d0 100%);
+  color: #065f46;
+  border-left: 4px solid #10b981;
+}
+
+.status-banner.warning {
+  background: linear-gradient(135deg, #fee2e2 0%, #fecaca 100%);
+  color: #991b1b;
+  border-left: 4px solid #ef4444;
+}
+
+.spinner {
+  width: 20px;
+  height: 20px;
+  border: 3px solid rgba(0, 0, 0, 0.1);
+  border-top-color: currentColor;
+  border-radius: 50%;
+  animation: spin 0.8s linear infinite;
+}
+
+@keyframes spin {
+  to { transform: rotate(360deg); }
+}
+
+.control-horizontal {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+.control-left {
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+}
+
+.control-right {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.input-group {
+  display: flex;
+  gap: 0.5rem;
+  margin-bottom: 0;
+}
+
+input[type="text"] {
+  flex: 1;
+  padding: 0.75rem;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+  font-size: 1rem;
+}
+
+input[type="text"]:disabled {
+  background: #f5f5f5;
+  cursor: not-allowed;
+}
+
+input[type="text"]:focus {
+  outline: none;
+  border-color: #10b981;
+}
+
+button {
+  padding: 0.75rem 1.5rem;
+  border: none;
+  border-radius: 4px;
+  font-size: 1rem;
+  font-weight: 500;
+  cursor: pointer;
+  transition: all 0.2s;
+}
+
+.btn-set-task {
+  background: #3b82f6;
+  color: white;
+  min-width: 120px;
+}
+
+.btn-set-task:hover:not(:disabled) {
+  background: #2563eb;
+}
+
+.btn-set-task:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.btn-start {
+  background: #10b981;
+  color: white;
+}
+
+.btn-start:hover:not(:disabled) {
+  background: #059669;
+}
+
+.btn-start:disabled {
+  background: #d1d5db;
+  cursor: not-allowed;
+}
+
+.btn-stop {
+  background: #ef4444;
+  color: white;
+}
+
+.btn-stop:hover {
+  background: #dc2626;
+}
+
+.btn-reset {
+  padding: 0.5rem 1rem;
+  background: #6b7280;
+  color: white;
+  font-size: 0.875rem;
+}
+
+.btn-reset:hover {
+  background: #4b5563;
+}
+
+.status {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  padding: 1rem;
+  border-radius: 4px;
+  margin-bottom: 1rem;
+}
+
+.status.recording {
+  background: #fee2e2;
+  color: #991b1b;
+}
+
+.status.recording.recording-active {
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+  background: #dc2626;
+  color: white;
+  padding: 1.5rem;
+  border: 4px solid #991b1b;
+  box-shadow: 0 4px 12px rgba(220, 38, 38, 0.4);
+  font-weight: 700;
+  font-size: 1rem;
+}
+
+.status.recording.recording-active .indicator {
+  width: 20px;
+  height: 20px;
+  background: #fef2f2;
+  animation: pulse-strong 1s ease-in-out infinite;
+}
+
+@keyframes pulse-strong {
+  0%, 100% { 
+    opacity: 1;
+    transform: scale(1);
+  }
+  50% { 
+    opacity: 0.7;
+    transform: scale(1.1);
+  }
+}
+
+.status.recording.recording-active .time-display {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+  font-size: 1.5rem;
+  font-weight: 700;
+  color: white;
+}
+
+.fps-display {
+  font-size: 1rem;
+  font-weight: 500;
+  opacity: 0.95;
+}
+
+.fps-warning {
+  color: #fef2f2;
+  animation: pulse-warning 1s ease-in-out infinite;
+}
+
+@keyframes pulse-warning {
+  0%, 100% { opacity: 1; }
+  50% { opacity: 0.5; }
+}
+
+.status.recording.recording-active .btn-stop {
+  align-self: stretch;
+}
+
+.ramp-up-countdown {
+  display: flex;
+  justify-content: center;
+  margin-bottom: 1rem;
+}
+
+.countdown-box {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  padding: 2rem 3rem;
+  background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
+  border: 4px solid #f59e0b;
+  border-radius: 16px;
+  box-shadow: 0 6px 20px rgba(245, 158, 11, 0.4);
+  min-width: 280px;
+  animation: pulse-warm 1.5s ease-in-out infinite;
+}
+
+@keyframes pulse-warm {
+  0%, 100% {
+    box-shadow: 0 6px 20px rgba(245, 158, 11, 0.4);
+  }
+  50% {
+    box-shadow: 0 6px 25px rgba(245, 158, 11, 0.6);
+  }
+}
+
+.countdown-label {
+  font-size: 1rem;
+  color: #92400e;
+  text-transform: uppercase;
+  letter-spacing: 1.5px;
+  font-weight: 800;
+  margin-bottom: 1rem;
+  text-align: center;
+}
+
+.countdown-value {
+  font-size: 4.5rem;
+  font-weight: 900;
+  color: #d97706;
+  font-family: 'Courier New', monospace;
+  line-height: 1;
+  text-shadow: 2px 2px 6px rgba(0, 0, 0, 0.15);
+  margin-bottom: 0.5rem;
+}
+
+.countdown-subtitle {
+  font-size: 0.875rem;
+  color: #78350f;
+  font-weight: 600;
+  font-style: italic;
+  text-align: center;
+  margin-top: 0.5rem;
+}
+
+.status.idle {
+  background: #f3f4f6;
+  color: #374151;
+}
+
+.indicator {
+  width: 12px;
+  height: 12px;
+  border-radius: 50%;
+  background: #ef4444;
+  animation: pulse 1.5s ease-in-out infinite;
+}
+
+@keyframes pulse {
+  0%, 100% { opacity: 1; }
+  50% { opacity: 0.5; }
+}
+
+.counter {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 0.75rem;
+  padding: 1.5rem;
+  background: linear-gradient(135deg, #f9fafb 0%, #f3f4f6 100%);
+  border-radius: 8px;
+  border: 2px solid #e5e7eb;
+  min-width: 200px;
+}
+
+.counter-label {
+  font-size: 0.75rem;
+  color: #6b7280;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+  font-weight: 600;
+}
+
+.counter-value {
+  font-size: 3rem;
+  font-weight: 700;
+  color: #10b981;
+  line-height: 1;
+}
+
+.time-display {
+  font-size: 1.5rem;
+  font-weight: 600;
+  font-family: 'Courier New', monospace;
+}
+
+.error-box {
+  padding: 1rem;
+  background: #fee2e2;
+  color: #991b1b;
+  border-radius: 4px;
+  border-left: 4px solid #ef4444;
+  font-size: 0.875rem;
+}
+
+.config-section {
+  margin-bottom: 1.5rem;
+}
+
+.config-section:last-child {
+  margin-bottom: 0;
+}
+
+.config-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+  gap: 1rem;
+}
+
+label {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+  font-size: 0.875rem;
+  color: #374151;
+  font-weight: 500;
+}
+
+select {
+  padding: 0.5rem;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+  font-size: 0.875rem;
+  background: white;
+}
+
+select:disabled {
+  background: #f5f5f5;
+  cursor: not-allowed;
+}
+
+/* Camera Layout */
+.camera-layout {
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+}
+
+.camera-base {
+  width: 100%;
+}
+
+.camera-wrist-container {
+  display: grid;
+  grid-template-columns: repeat(2, 1fr);
+  gap: 1.5rem;
+}
+
+.camera-wrist {
+  width: 100%;
+}
+
+.camera {
+  border: 1px solid #e5e7eb;
+  border-radius: 4px;
+  overflow: hidden;
+}
+
+.camera h3 {
+  padding: 0.75rem;
+  background: #f9fafb;
+  border-bottom: 1px solid #e5e7eb;
+  margin: 0;
+}
+
+.camera img {
+  width: 100%;
+  height: auto;
+  display: block;
+  background: #000;
+  min-height: 300px;
+  object-fit: cover;
+}
+
+.camera-placeholder {
+  text-align: center;
+  padding: 4rem 2rem;
+  background: #f9fafb;
+  border-radius: 4px;
+  border: 2px dashed #d1d5db;
+}
+
+.camera-placeholder p {
+  margin: 0.5rem 0;
+  font-size: 1rem;
+  color: #6b7280;
+}
+
+.camera-placeholder p:first-child {
+  font-size: 1.25rem;
+  font-weight: 500;
+  color: #374151;
+}
+
+.hint {
+  margin-top: 0.5rem;
+  font-size: 0.75rem;
+  color: #6b7280;
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  flex-wrap: wrap;
+}
+

examples/openarms_web_interface/App.jsx

@@ -0,0 +1,857 @@
+import { useState, useEffect, useCallback, useRef } from 'react';
+import './App.css';
+
+const API_BASE = 'http://localhost:8000/api';
+
+function App() {
+  // State
+  const [task, setTask] = useState('');
+  const [isRecording, setIsRecording] = useState(false);
+  const [isInitializing, setIsInitializing] = useState(false);
+  const [isEncoding, setIsEncoding] = useState(false);
+  const [isUploading, setIsUploading] = useState(false);
+  const [robotsReady, setRobotsReady] = useState(false);
+  const [elapsedTime, setElapsedTime] = useState(0);
+  const [currentFps, setCurrentFps] = useState(0);
+  const [loopFps, setLoopFps] = useState(0);
+  const [episodeCount, setEpisodeCount] = useState(0);
+  const [error, setError] = useState(null);
+  const [statusMessage, setStatusMessage] = useState('Ready');
+  const [uploadStatus, setUploadStatus] = useState(null);
+  const [rampUpRemaining, setRampUpRemaining] = useState(0);
+  const [movingToZero, setMovingToZero] = useState(false);
+  const [configExpanded, setConfigExpanded] = useState(false);
+  const [latestRepoId, setLatestRepoId] = useState(null);
+
+  // Configuration
+  const [config, setConfig] = useState({
+    leader_type: 'openarms',  // 'openarms' or 'openarms_mini'
+    leader_left: 'can0',
+    leader_right: 'can1',
+    follower_left: 'can2',
+    follower_right: 'can3',
+    left_wrist: '/dev/video0',
+    right_wrist: '/dev/video1',
+    base: '/dev/video4'
+  });
+
+  // Available options
+  const [availableCameras, setAvailableCameras] = useState([]);
+  const [availableUsbPorts, setAvailableUsbPorts] = useState([]);
+  const canInterfaces = ['can0', 'can1', 'can2', 'can3'];
+
+  const statusIntervalRef = useRef(null);
+  const hasInitializedRef = useRef(false);
+
+  const loadConfig = () => {
+    try {
+      const saved = localStorage.getItem('openarms_config');
+      if (saved) {
+        const loadedConfig = JSON.parse(saved);
+        setConfig(prev => ({ ...prev, ...loadedConfig }));
+      }
+    } catch (e) {
+      console.error('Load config error:', e);
+    }
+  };
+
+  const saveConfig = (newConfig) => {
+    try {
+      localStorage.setItem('openarms_config', JSON.stringify(newConfig || config));
+    } catch (e) {
+      console.error('Save config error:', e);
+    }
+  };
+
+  // Fetch status periodically
+  const fetchStatus = async () => {
+    try {
+      const response = await fetch(`${API_BASE}/status`);
+      const data = await response.json();
+
+      setIsRecording(data.is_recording);
+      setIsInitializing(data.is_initializing);
+      setIsEncoding(data.is_encoding);
+      setIsUploading(data.is_uploading);
+      setRobotsReady(data.robots_ready);
+      setElapsedTime(data.elapsed_time);
+      setCurrentFps(data.current_fps || 0);
+      setLoopFps(data.loop_fps || 0);
+      setEpisodeCount(data.episode_count);
+      setError(data.error);
+      setStatusMessage(data.status_message || 'Ready');
+      setUploadStatus(data.upload_status);
+      setRampUpRemaining(data.ramp_up_remaining || 0);
+      setMovingToZero(data.moving_to_zero || false);
+      
+      // Track the latest repo_id from the backend
+      if (data.latest_repo_id) {
+        setLatestRepoId(data.latest_repo_id);
+      }
+
+      if (data.config) {
+        // Only merge server config if we don't have a saved config (first load)
+        if (!localStorage.getItem('openarms_config')) {
+          setConfig(prev => {
+            const merged = { ...data.config, ...prev };
+            localStorage.setItem('openarms_config', JSON.stringify(merged));
+            return merged;
+          });
+        }
+      }
+    } catch (e) {
+      console.error('Failed to fetch status:', e);
+    }
+  };
+
+  const setupRobots = async () => {
+    // Show warning to verify camera positions
+    const confirmed = window.confirm(
+      '⚠️ IMPORTANT: Before connecting robots, please verify:\n\n' +
+      '📹 Check that cameras are correctly positioned:\n' +
+      '   • LEFT wrist camera is actually on the LEFT arm\n' +
+      '   • RIGHT wrist camera is actually on the RIGHT arm\n' +
+      '   • BASE camera is actually the BASE/overhead camera\n\n' +
+      'Incorrect camera positioning will result in invalid training data!\n\n' +
+      'Click OK to continue with robot setup, or Cancel to review configuration.'
+    );
+    
+    if (!confirmed) {
+      return; // User cancelled, don't proceed
+    }
+    
+    setError(null);
+    try {
+      const response = await fetch(`${API_BASE}/robots/setup`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify(config)
+      });
+
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to setup robots');
+      }
+
+      await response.json();
+      saveConfig(config);
+    } catch (e) {
+      setError(`Robot setup failed: ${e.message}`);
+    }
+  };
+
+  // Disconnect robots
+  const disconnectRobots = async () => {
+    try {
+      await fetch(`${API_BASE}/robots/disconnect`, { method: 'POST' });
+      setRobotsReady(false);
+    } catch (e) {
+      console.error('Failed to disconnect robots:', e);
+    }
+  };
+
+  // Discover cameras
+  const discoverCameras = async () => {
+    try {
+      const response = await fetch(`${API_BASE}/cameras/discover`);
+      const data = await response.json();
+      const cameras = data.cameras || [];
+      setAvailableCameras(cameras);
+
+      // Get list of valid camera IDs
+      const validCameraIds = cameras.map(cam => String(cam.id));
+
+      // Auto-fix config if current values are invalid or not set
+      const updated = { ...config };
+      let changed = false;
+
+      // Auto-fix invalid camera config
+      if (!config.left_wrist || !validCameraIds.includes(config.left_wrist)) {
+        if (cameras.length >= 1) {
+          updated.left_wrist = String(cameras[0].id);
+          changed = true;
+        }
+      }
+
+      if (!config.right_wrist || !validCameraIds.includes(config.right_wrist)) {
+        if (cameras.length >= 2) {
+          updated.right_wrist = String(cameras[1].id);
+          changed = true;
+        }
+      }
+
+      if (!config.base || !validCameraIds.includes(config.base)) {
+        if (cameras.length >= 3) {
+          updated.base = String(cameras[2].id);
+          changed = true;
+        }
+      }
+
+      if (changed) {
+        setConfig(updated);
+        saveConfig(updated);
+      }
+
+      if (cameras.length === 0) {
+        setError('No cameras detected! Please connect cameras and refresh.');
+      }
+    } catch (e) {
+      console.error('Failed to discover cameras:', e);
+      setError(`Camera discovery failed: ${e.message}`);
+    }
+  };
+
+  // Discover USB ports
+  const discoverUsbPorts = async () => {
+    try {
+      const response = await fetch(`${API_BASE}/usb/discover`);
+      const data = await response.json();
+      const ports = data.ports || [];
+      setAvailableUsbPorts(ports);
+
+      // Auto-fix config if OpenArms Mini is selected and ports are invalid
+      if (config.leader_type === 'openarms_mini') {
+        const updated = { ...config };
+        let changed = false;
+
+        if (ports.length >= 1 && !ports.includes(config.leader_left)) {
+          updated.leader_left = ports[0];
+          changed = true;
+        }
+
+        if (ports.length >= 2 && !ports.includes(config.leader_right)) {
+          updated.leader_right = ports[1];
+          changed = true;
+        }
+
+        if (changed) {
+          setConfig(updated);
+          saveConfig(updated);
+        }
+      }
+
+      if (ports.length === 0) {
+        console.warn('No USB ports detected for OpenArms Mini');
+      }
+    } catch (e) {
+      console.error('Failed to discover USB ports:', e);
+    }
+  };
+
+  // Set task only (for pedal use)
+  const setTaskOnly = async () => {
+    if (!task.trim()) {
+      setError('Please enter a task description');
+      return;
+    }
+
+    setError(null);
+
+    try {
+      const response = await fetch(`${API_BASE}/recording/set-task`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ task, ...config })
+      });
+
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to set task');
+      }
+
+      const result = await response.json();
+      setStatusMessage(result.message || `Task set: ${task}`);
+      saveConfig(config);
+      
+      // Clear success message after 3 seconds
+      setTimeout(() => {
+        if (!isRecording && !isInitializing) {
+          setStatusMessage('Ready');
+        }
+      }, 3000);
+    } catch (e) {
+      setError(e.message);
+    }
+  };
+
+  // Start recording
+  const startRecording = async () => {
+    if (!task.trim()) {
+      setError('Please enter a task description');
+      return;
+    }
+
+    setError(null);
+
+    try {
+      const response = await fetch(`${API_BASE}/recording/start`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ task, ...config })
+      });
+
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to start recording');
+      }
+
+      await response.json();
+      saveConfig(config);
+    } catch (e) {
+      setError(e.message);
+    }
+  };
+
+  // Stop recording
+  const stopRecording = async () => {
+    try {
+      const response = await fetch(`${API_BASE}/recording/stop`, {
+        method: 'POST'
+      });
+
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to stop recording');
+      }
+
+      const data = await response.json();
+      setError(null);
+      // Update latest repo_id after recording
+      if (data.dataset_name) {
+        setLatestRepoId(`lerobot-data-collection/${data.dataset_name}`);
+      }
+    } catch (e) {
+      setError(e.message);
+    }
+  };
+
+  const deleteLatestEpisode = async () => {
+    if (!latestRepoId) {
+      setError('No episode to delete');
+      return;
+    }
+    
+    const confirmed = window.confirm(
+      `WARNING: This will permanently delete the repository:\n\n${latestRepoId}\n\nThis action cannot be undone. Continue?`
+    );
+    
+    if (!confirmed) {
+      return;
+    }
+    
+    try {
+      const response = await fetch(`${API_BASE}/recording/delete-latest`, { method: 'POST' });
+      
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to delete episode');
+      }
+
+      const data = await response.json();
+      setLatestRepoId(null);
+      setEpisodeCount(Math.max(0, episodeCount - 1));
+      setStatusMessage(`Deleted: ${data.deleted_repo}`);
+      
+      setTimeout(() => {
+        if (!isRecording && !isInitializing) {
+          setStatusMessage('Ready');
+        }
+      }, 3000);
+    } catch (e) {
+      setError(`Delete failed: ${e.message}`);
+    }
+  };
+
+  // Reset counter
+  const resetCounter = async () => {
+    try {
+      await fetch(`${API_BASE}/counter/reset`, { method: 'POST' });
+      setEpisodeCount(0);
+    } catch (e) {
+      console.error('Failed to reset counter:', e);
+    }
+  };
+
+  // Move robot to zero position
+  const moveToZero = async () => {
+    setError(null);
+    try {
+      const response = await fetch(`${API_BASE}/robots/move-to-zero`, { method: 'POST' });
+      if (!response.ok) {
+        const data = await response.json();
+        throw new Error(data.detail || 'Failed to move to zero position');
+      }
+      await response.json();
+    } catch (e) {
+      setError(`Move to zero failed: ${e.message}`);
+    }
+  };
+
+  // Format time as MM:SS
+  const formatTime = (seconds) => {
+    const mins = Math.floor(seconds / 60);
+    const secs = Math.floor(seconds % 60);
+    return `${mins.toString().padStart(2, '0')}:${secs.toString().padStart(2, '0')}`;
+  };
+
+  // Update config and save
+  const updateConfig = (key, value) => {
+    const updated = { ...config, [key]: value };
+    setConfig(updated);
+    saveConfig(updated);
+  };
+
+  // Initialize on mount only
+  useEffect(() => {
+    // Prevent double-initialization in development
+    if (hasInitializedRef.current) {
+      return;
+    }
+    hasInitializedRef.current = true;
+
+    loadConfig();
+    discoverCameras();
+    discoverUsbPorts();
+    fetchStatus();
+    statusIntervalRef.current = setInterval(fetchStatus, 1000);
+
+    return () => {
+      if (statusIntervalRef.current) {
+        clearInterval(statusIntervalRef.current);
+      }
+    };
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []); // Run only once on mount
+
+  // Discover USB ports when leader type changes to Mini
+  useEffect(() => {
+    if (config.leader_type === 'openarms_mini') {
+      discoverUsbPorts();
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [config.leader_type]);
+
+  return (
+    <main>
+      <header>
+        <h1>OpenArms Recording</h1>
+      </header>
+
+      <div className="container">
+        {/* Left Column: Configuration and Recording Control */}
+        <div className="left-column">
+          {/* Configuration Panel */}
+          <section className="panel config-panel">
+          <div
+            className="config-header"
+            onClick={() => setConfigExpanded(!configExpanded)}
+            role="button"
+            tabIndex={0}
+            onKeyDown={(e) => e.key === 'Enter' && setConfigExpanded(!configExpanded)}
+          >
+            <h2>⚙️ Configuration</h2>
+            <span className="toggle-icon">{configExpanded ? '▼' : '▶'}</span>
+          </div>
+
+          {configExpanded && (
+            <div className="config-content">
+              {/* Robot Setup */}
+              <div className="config-section">
+                <h3>🤖 Robot Setup</h3>
+                <div className="robot-setup">
+                  {robotsReady ? (
+                    <div className="robot-status ready">
+                      <span>✅ Robots Ready - Recording will start instantly</span>
+                      <button onClick={disconnectRobots} className="btn-disconnect">
+                        Disconnect Robots
+                      </button>
+                    </div>
+                  ) : (
+                    <div className="robot-status not-ready">
+                      <span>⚠️ Robots not initialized - Recording will take ~10 seconds</span>
+                      <button
+                        onClick={setupRobots}
+                        disabled={isRecording || isInitializing}
+                        className="btn-setup"
+                      >
+                        🚀 Setup Robots
+                      </button>
+                    </div>
+                  )}
+                </div>
+              </div>
+
+              {/* Leader Type Selection */}
+              <div className="config-section">
+                <h3>🎮 Leader Type</h3>
+                <div className="config-grid">
+                  <label style={{gridColumn: '1 / -1'}}>
+                    Leader Arm Type
+                    <select
+                      value={config.leader_type}
+                      onChange={(e) => updateConfig('leader_type', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      <option value="openarms">OpenArms (CAN Bus - Damiao Motors)</option>
+                      <option value="openarms_mini">OpenArms Mini (USB - Feetech Motors)</option>
+                    </select>
+                  </label>
+                </div>
+              </div>
+
+              {/* Leader Interfaces (CAN or USB based on type) */}
+              <div className="config-section">
+                <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: '0.5rem' }}>
+                  <h3>
+                    {config.leader_type === 'openarms_mini' 
+                      ? `Leader Ports (USB/Serial) ${availableUsbPorts.length > 0 ? `(${availableUsbPorts.length} detected)` : ''}` 
+                      : 'Leader Interfaces (CAN)'}
+                  </h3>
+                  {config.leader_type === 'openarms_mini' && (
+                    <button
+                      onClick={discoverUsbPorts}
+                      className="btn-refresh"
+                      disabled={isRecording || robotsReady}
+                    >
+                      🔄 Refresh
+                    </button>
+                  )}
+                </div>
+                
+                <div className="config-grid">
+                  <label>
+                    Leader Left
+                    <select
+                      value={config.leader_left}
+                      onChange={(e) => updateConfig('leader_left', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {config.leader_type === 'openarms_mini' ? (
+                        availableUsbPorts.length > 0 ? (
+                          availableUsbPorts.map((port) => (
+                            <option key={port} value={port}>{port}</option>
+                          ))
+                        ) : (
+                          <option value="">No USB ports detected</option>
+                        )
+                      ) : (
+                        canInterfaces.map((iface) => (
+                          <option key={iface} value={iface}>{iface}</option>
+                        ))
+                      )}
+                    </select>
+                  </label>
+
+                  <label>
+                    Leader Right
+                    <select
+                      value={config.leader_right}
+                      onChange={(e) => updateConfig('leader_right', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {config.leader_type === 'openarms_mini' ? (
+                        availableUsbPorts.length > 0 ? (
+                          availableUsbPorts.map((port) => (
+                            <option key={port} value={port}>{port}</option>
+                          ))
+                        ) : (
+                          <option value="">No USB ports detected</option>
+                        )
+                      ) : (
+                        canInterfaces.map((iface) => (
+                          <option key={iface} value={iface}>{iface}</option>
+                        ))
+                      )}
+                    </select>
+                  </label>
+                </div>
+              </div>
+
+              {/* Follower CAN Interfaces */}
+              <div className="config-section">
+                <h3>Follower Interfaces (CAN)</h3>
+                
+                <div className="config-grid">
+                  <label>
+                    Follower Left
+                    <select
+                      value={config.follower_left}
+                      onChange={(e) => updateConfig('follower_left', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {canInterfaces.map((iface) => (
+                        <option key={iface} value={iface}>{iface}</option>
+                      ))}
+                    </select>
+                  </label>
+
+                  <label>
+                    Follower Right
+                    <select
+                      value={config.follower_right}
+                      onChange={(e) => updateConfig('follower_right', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {canInterfaces.map((iface) => (
+                        <option key={iface} value={iface}>{iface}</option>
+                      ))}
+                    </select>
+                  </label>
+                </div>
+              </div>
+
+              {/* Camera Configuration */}
+              <div className="config-section">
+                <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: '0.5rem' }}>
+                  <h3>Cameras {availableCameras.length > 0 && `(${availableCameras.length} detected)`}</h3>
+                  <button
+                    onClick={discoverCameras}
+                    className="btn-refresh"
+                    disabled={isRecording || robotsReady}
+                  >
+                    🔄 Refresh
+                  </button>
+                </div>
+                <div className="config-grid">
+                  <label>
+                    Left Wrist
+                    <select
+                      value={config.left_wrist}
+                      onChange={(e) => updateConfig('left_wrist', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {availableCameras.map((cam) => (
+                        <option key={cam.id} value={String(cam.id)}>
+                          {cam.name || `Camera @ ${cam.id}`}
+                        </option>
+                      ))}
+                    </select>
+                  </label>
+
+                  <label>
+                    Right Wrist
+                    <select
+                      value={config.right_wrist}
+                      onChange={(e) => updateConfig('right_wrist', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {availableCameras.map((cam) => (
+                        <option key={cam.id} value={String(cam.id)}>
+                          {cam.name || `Camera @ ${cam.id}`}
+                        </option>
+                      ))}
+                    </select>
+                  </label>
+
+                  <label>
+                    Base Camera
+                    <select
+                      value={config.base}
+                      onChange={(e) => updateConfig('base', e.target.value)}
+                      disabled={isRecording || robotsReady}
+                    >
+                      {availableCameras.map((cam) => (
+                        <option key={cam.id} value={String(cam.id)}>
+                          {cam.name || `Camera @ ${cam.id}`}
+                        </option>
+                      ))}
+                    </select>
+                  </label>
+                </div>
+              </div>
+            </div>
+          )}
+        </section>
+
+        {/* Control Panel */}
+        <section className="panel control-panel">
+          <h2>🎬 Recording Control</h2>
+
+          {/* Status Banner - Always show important statuses */}
+          {isInitializing && (
+            <div className="status-banner initializing">
+              <div className="spinner"></div>
+              <span>{statusMessage}</span>
+            </div>
+          )}
+
+          {isEncoding && (
+            <div className="status-banner encoding">
+              <div className="spinner"></div>
+              <span>📹 {statusMessage}</span>
+            </div>
+          )}
+
+          {isUploading && (
+            <div className="status-banner uploading">
+              <div className="spinner"></div>
+              <span>☁️ {statusMessage}</span>
+            </div>
+          )}
+
+          {uploadStatus && !isRecording && !isEncoding && !isUploading && (
+            <div className={`status-banner ${uploadStatus.startsWith('✓') ? 'success' : 'warning'}`}>
+              <span>{uploadStatus}</span>
+            </div>
+          )}
+
+          <div className="control-horizontal">
+            {/* Task Input and Status */}
+            <div className="control-left">
+              <div className="input-group">
+                <input
+                  type="text"
+                  value={task}
+                  onChange={(e) => setTask(e.target.value)}
+                  placeholder="Task description (e.g., 'pick and place')"
+                  disabled={isRecording || isInitializing || isEncoding || isUploading}
+                  onKeyPress={(e) => {
+                    if (e.key === 'Enter' && robotsReady) {
+                      setTaskOnly();
+                    }
+                  }}
+                />
+                <button
+                  onClick={setTaskOnly}
+                  disabled={isRecording || isInitializing || isEncoding || isUploading || !robotsReady}
+                  className="btn-set-task"
+                  title={!robotsReady ? 'Please setup robots first' : 'Store task for pedal use (Enter key)'}
+                >
+                  💾 Set Task
+                </button>
+                <button
+                  onClick={startRecording}
+                  disabled={isRecording || isInitializing || isEncoding || isUploading || !robotsReady}
+                  className="btn-start"
+                  title={!robotsReady ? 'Please setup robots first' : ''}
+                >
+                  {isInitializing
+                    ? '⏳ Initializing...'
+                    : isRecording
+                    ? '⏺ Recording...'
+                    : robotsReady
+                    ? '⏺ Start Recording'
+                    : '⏺ Setup Robots First'}
+                </button>
+              </div>
+
+              {/* Ramp-up Countdown */}
+              {isRecording && rampUpRemaining > 0 && (
+                <div className="ramp-up-countdown">
+                  <div className="countdown-box">
+                    <div className="countdown-label">⚡ WARMING UP - PID RAMP-UP</div>
+                    <div className="countdown-value">{rampUpRemaining.toFixed(1)}s</div>
+                    <div className="countdown-subtitle">Recording will start automatically...</div>
+                  </div>
+                </div>
+              )}
+
+              {/* Recording Status - Only show after ramp-up */}
+              {isRecording && rampUpRemaining <= 0 && (
+                <div className="status recording recording-active">
+                  <div className="indicator"></div>
+                  <div className="time-display">
+                    <span>{formatTime(elapsedTime)}</span>
+                    <span className="fps-display">
+                      Loop: {loopFps.toFixed(1)} Hz
+                      {loopFps > 0 && loopFps < 29 && <span className="fps-warning"> ⚠️</span>}
+                    </span>
+                    <span className="fps-display">Recording: {currentFps.toFixed(1)} FPS</span>
+                  </div>
+                  <button onClick={stopRecording} className="btn-stop">
+                    ⏹ Stop
+                  </button>
+                </div>
+              )}
+            </div>
+
+            {/* Episode Counter */}
+            <div className="control-right">
+              <div className="counter">
+                <div className="counter-label">Episodes Recorded</div>
+                <div className="counter-value">{episodeCount}</div>
+                <button onClick={resetCounter} className="btn-reset">
+                  Reset
+                </button>
+              </div>
+            </div>
+          </div>
+
+          {/* Delete Latest Episode Button */}
+          {!isRecording && !isInitializing && latestRepoId && (
+            <div className="delete-episode-section">
+              <button 
+                onClick={deleteLatestEpisode}
+                className="btn-delete"
+                title="Delete the latest recorded episode from HuggingFace Hub"
+              >
+                Delete Latest Episode
+              </button>
+              <div className="delete-info">Will delete: {latestRepoId}</div>
+            </div>
+          )}
+
+          {/* Move to Zero Button */}
+          {robotsReady && !isRecording && !isInitializing && (
+            <div className="zero-position-section">
+              <button 
+                onClick={moveToZero} 
+                disabled={movingToZero}
+                className="btn-zero-large"
+                title="Move both leader and follower robots to zero position (2s)"
+              >
+                {movingToZero ? '⏳ Moving to Zero Position...' : '🎯 Move to Zero Position (Leader + Follower)'}
+              </button>
+            </div>
+          )}
+
+          {/* Error Display */}
+          {error && (
+            <div className="error-box">
+              ⚠️ {error}
+            </div>
+          )}
+        </section>
+        </div>
+
+        {/* Right Column: Camera Feeds */}
+        <div className="right-column">
+          <section className="panel cameras">
+          <h2>📹 Camera Views</h2>
+          {robotsReady || isRecording || isInitializing ? (
+            <div className="camera-layout">
+              {/* Base camera - full width */}
+              <div className="camera camera-base">
+                <h3>Base Camera</h3>
+                <img src={`${API_BASE}/camera/stream/base`} alt="Base Camera" />
+              </div>
+
+              {/* Wrist cameras - side by side */}
+              <div className="camera-wrist-container">
+                <div className="camera camera-wrist">
+                  <h3>Left Wrist</h3>
+                  <img src={`${API_BASE}/camera/stream/left_wrist`} alt="Left Wrist Camera" />
+                </div>
+
+                <div className="camera camera-wrist">
+                  <h3>Right Wrist</h3>
+                  <img src={`${API_BASE}/camera/stream/right_wrist`} alt="Right Wrist Camera" />
+                </div>
+              </div>
+            </div>
+          ) : (
+            <div className="camera-placeholder">
+              <p>📷 Camera feeds will appear when robots are set up</p>
+              <p className="hint">Click "Setup Robots" above to preview camera feeds</p>
+            </div>
+          )}
+        </section>
+        </div>
+
+      </div>
+    </main>
+  );
+}
+
+export default App;
+

examples/openarms_web_interface/README.md

@@ -0,0 +1,41 @@
+# OpenArms Web Recording Interface
+
+A web interface for recording OpenArms datasets.
+
+## Installation
+
+```bash
+cd examples/openarms_web_interface
+npm install
+```
+
+## Usage
+
+**Start everything with one command:**
+
+```bash
+./launch.sh
+```
+
+This will:
+- Start the FastAPI backend on port 8000
+- Start the React frontend on port 5173
+- Show live logs from both services
+
+Then open your browser to: **http://localhost:5173**
+
+**Stop with:** `Ctrl+C`
+
+---
+
+## Workflow
+
+1. **Configure CAN interfaces** and **camera paths** in the dropdowns
+2. Click **"Setup Robots"** to initialize (once at start)
+3. Enter a **task description**
+4. Click **"Start Recording"** to begin an episode
+5. Click **"Stop Recording"** when done
+6. Dataset is automatically encoded and uploaded to HuggingFace Hub as **private**
+7. Repeat steps 3-6 for more episodes (no need to re-setup robots!)
+
+---

examples/openarms_web_interface/index.html

@@ -0,0 +1,12 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>OpenArms Recording Interface</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/main.jsx"></script>
+  </body>
+</html>

examples/openarms_web_interface/launch.sh

@@ -0,0 +1,142 @@
+#!/bin/bash
+
+# OpenArms Web Interface Launcher
+# Starts Rerun viewer, FastAPI backend, and React frontend
+
+set -e
+
+# Colors for output
+GREEN='\033[0;32m'
+BLUE='\033[0;34m'
+YELLOW='\033[1;33m'
+RED='\033[0;31m'
+NC='\033[0m' # No Color
+
+# Get script directory
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+cd "$SCRIPT_DIR"
+
+echo -e "${BLUE}╔════════════════════════════════════════╗${NC}"
+echo -e "${BLUE}║   OpenArms Web Recording Interface    ║${NC}"
+echo -e "${BLUE}╚════════════════════════════════════════╝${NC}"
+echo ""
+
+# Function to cleanup on exit
+cleanup() {
+    echo ""
+    echo -e "${YELLOW}Shutting down services...${NC}"
+    
+    # Kill all child processes
+    pkill -P $$ 2>/dev/null || true
+    
+    # Kill specific services by port
+    lsof -ti:8000 | xargs kill -9 2>/dev/null || true  # Backend
+    lsof -ti:5173 | xargs kill -9 2>/dev/null || true  # Frontend
+    lsof -ti:9876 | xargs kill -9 2>/dev/null || true  # Rerun (if spawned)
+    
+    echo -e "${GREEN}✓ Services stopped${NC}"
+    exit 0
+}
+
+# Register cleanup on script exit
+trap cleanup EXIT INT TERM
+
+# Check if required commands exist
+command -v rerun >/dev/null 2>&1 || { 
+    echo -e "${RED}✗ Error: 'rerun' not found. Please install: pip install rerun-sdk${NC}"
+    exit 1
+}
+
+command -v python >/dev/null 2>&1 || { 
+    echo -e "${RED}✗ Error: 'python' not found${NC}"
+    exit 1
+}
+
+command -v npm >/dev/null 2>&1 || { 
+    echo -e "${RED}✗ Error: 'npm' not found${NC}"
+    exit 1
+}
+
+# Check if node_modules exists
+if [ ! -d "node_modules" ]; then
+    echo -e "${YELLOW}⚠ node_modules not found. Running npm install...${NC}"
+    npm install
+    echo -e "${GREEN}✓ Dependencies installed${NC}"
+    echo ""
+fi
+
+echo -e "${GREEN}Starting services...${NC}"
+echo ""
+
+# 1. Start FastAPI backend (Rerun will start when recording begins)
+echo -e "${BLUE}[1/2]${NC} Starting FastAPI backend on port 8000..."
+cd "$SCRIPT_DIR"
+
+# Use Python from current environment (if lerobot env is active, it will use that)
+# Otherwise, check if we need to use conda run
+if [[ "$CONDA_DEFAULT_ENV" == "lerobot" ]]; then
+    # Already in lerobot environment
+    echo -e "${GREEN}✓ Using active lerobot environment${NC}"
+    PYTHON_CMD="python"
+elif command -v conda >/dev/null 2>&1 && conda env list | grep -q "^lerobot "; then
+    # lerobot env exists but not active - use conda run
+    echo -e "${YELLOW}Using conda run with lerobot environment...${NC}"
+    PYTHON_CMD="conda run -n lerobot --no-capture-output python"
+else
+    # Fall back to system python
+    echo -e "${YELLOW}⚠ Warning: lerobot environment not found, using system python${NC}"
+    PYTHON_CMD="python"
+fi
+
+$PYTHON_CMD web_record_server.py > /tmp/openarms_backend.log 2>&1 &
+BACKEND_PID=$!
+sleep 3
+
+if ps -p $BACKEND_PID > /dev/null; then
+    echo -e "${GREEN}✓ Backend started${NC} (PID: $BACKEND_PID)"
+    echo -e "      URL: ${BLUE}http://localhost:8000${NC}"
+else
+    echo -e "${RED}✗ Failed to start backend${NC}"
+    echo -e "${YELLOW}Check logs: tail -f /tmp/openarms_backend.log${NC}"
+    exit 1
+fi
+echo ""
+
+# 2. Start React frontend
+echo -e "${BLUE}[2/2]${NC} Starting React frontend on port 5173..."
+cd "$SCRIPT_DIR"
+npm run dev > /tmp/openarms_frontend.log 2>&1 &
+FRONTEND_PID=$!
+sleep 3
+
+if ps -p $FRONTEND_PID > /dev/null; then
+    echo -e "${GREEN}✓ Frontend started${NC} (PID: $FRONTEND_PID)"
+    echo -e "      URL: ${BLUE}http://localhost:5173${NC}"
+else
+    echo -e "${RED}✗ Failed to start frontend${NC}"
+    echo -e "${YELLOW}Check logs: tail -f /tmp/openarms_frontend.log${NC}"
+    exit 1
+fi
+echo ""
+
+# Display status
+echo -e "${GREEN}╔════════════════════════════════════════╗${NC}"
+echo -e "${GREEN}║     All services running! 🚀           ║${NC}"
+echo -e "${GREEN}╚════════════════════════════════════════╝${NC}"
+echo ""
+echo -e "🔧 ${BLUE}Backend:${NC}  http://localhost:8000"
+echo -e "🌐 ${BLUE}Frontend:${NC} http://localhost:5173"
+echo -e "📊 ${BLUE}Rerun:${NC}    Will spawn automatically when recording starts"
+echo ""
+echo -e "${YELLOW}Open your browser to:${NC} ${BLUE}http://localhost:5173${NC}"
+echo ""
+echo -e "${YELLOW}Logs:${NC}"
+echo -e "  • Backend:  tail -f /tmp/openarms_backend.log"
+echo -e "  • Frontend: tail -f /tmp/openarms_frontend.log"
+echo ""
+echo -e "${RED}Press Ctrl+C to stop all services${NC}"
+echo ""
+
+# Keep script running and wait for any service to exit
+wait
+

examples/openarms_web_interface/main.jsx

@@ -0,0 +1,7 @@
+import { createRoot } from 'react-dom/client'
+import App from './App.jsx'
+
+createRoot(document.getElementById('root')).render(
+  <App />
+)
+

examples/openarms_web_interface/package.json

@@ -0,0 +1,21 @@
+{
+  "name": "openarms-web-interface",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1"
+  },
+  "devDependencies": {
+    "@types/react": "^18.3.12",
+    "@types/react-dom": "^18.3.1",
+    "@vitejs/plugin-react": "^4.3.4",
+    "vite": "^6.0.1"
+  }
+}

examples/openarms_web_interface/vite.config.js

@@ -0,0 +1,17 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [react()],
+  server: {
+    port: 5173,
+    strictPort: false,
+    host: true,
+    open: false
+  },
+  build: {
+    outDir: 'dist',
+    sourcemap: true
+  }
+})

examples/phone_to_so100/evaluate.py

@@ -52,125 +52,114 @@ 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,
-)
 
-robot = SO100Follower(robot_config)
-
-# 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()),
-)
-
-# 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,
-        ),
-    ),
-    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,
+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,
     )
 
-    # 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")
+    robot = SO100Follower(robot_config)
+
+    # 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()),
+    )
+
+    # 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,
+            ),
+        ),
+        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,
@@ -179,21 +168,40 @@ for episode_idx in range(NUM_EPISODES):
             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
+        # 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,
+            )
 
-    # Save episode
-    dataset.save_episode()
-    episode_idx += 1
+        if events["rerecord_episode"]:
+            log_say("Re-record episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue
 
-# Clean up
-log_say("Stop recording")
-robot.disconnect()
-listener.stop()
+        # Save episode
+        dataset.save_episode()
+        episode_idx += 1
 
-dataset.finalize()
-dataset.push_to_hub()
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    listener.stop()
+
+    dataset.finalize()
+    dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/record.py

@@ -50,133 +50,122 @@ 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
 
-# Initialize the robot and teleoperator
-robot = SO100Follower(robot_config)
-phone = Phone(teleop_config)
+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
 
-# 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()),
-)
+    # Initialize the robot and teleoperator
+    robot = SO100Follower(robot_config)
+    phone = Phone(teleop_config)
 
-# 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,
-        ),
-    ),
-    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,
+    # 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()),
     )
 
-    # 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")
+    # 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,
+            ),
+        ),
+        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,
-            control_time_s=RESET_TIME_SEC,
+            dataset=dataset,
+            control_time_s=EPISODE_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
             teleop_action_processor=phone_to_robot_ee_pose_processor,
@@ -184,22 +173,42 @@ while episode_idx < NUM_EPISODES and not events["stop_recording"]:
             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
+        # 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,
+            )
 
-    # Save episode
-    dataset.save_episode()
-    episode_idx += 1
+        if events["rerecord_episode"]:
+            log_say("Re-recording episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue
 
-# Clean up
-log_say("Stop recording")
-robot.disconnect()
-phone.disconnect()
-listener.stop()
+        # Save episode
+        dataset.save_episode()
+        episode_idx += 1
 
-dataset.finalize()
-dataset.push_to_hub()
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    phone.disconnect()
+    listener.stop()
+
+    dataset.finalize()
+    dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/replay.py

@@ -29,72 +29,78 @@ 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 busy_wait
+from lerobot.utils.robot_utils import precise_sleep
 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
-)
 
-# Initialize the robot
-robot = SO100Follower(robot_config)
+def main():
+    # Initialize the robot config
+    robot_config = SO100FollowerConfig(
+        port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
+    )
 
-# 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()),
-)
+    # Initialize the robot
+    robot = SO100Follower(robot_config)
 
-# 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,
-)
+    # 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()),
+    )
 
-# 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)
+    # 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,
+    )
 
-# Connect to the robot
-robot.connect()
+    # 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)
 
-if not robot.is_connected:
-    raise ValueError("Robot is not connected!")
+    # Connect to the robot
+    robot.connect()
 
-print("Starting replay loop...")
-log_say(f"Replaying episode {EPISODE_IDX}")
-for idx in range(len(episode_frames)):
-    t0 = time.perf_counter()
+    if not robot.is_connected:
+        raise ValueError("Robot is not connected!")
 
-    # Get recorded action from dataset
-    ee_action = {
-        name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-    }
+    print("Starting replay loop...")
+    log_say(f"Replaying episode {EPISODE_IDX}")
+    for idx in range(len(episode_frames)):
+        t0 = time.perf_counter()
 
-    # Get robot observation
-    robot_obs = robot.get_observation()
+        # Get recorded action from dataset
+        ee_action = {
+            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+        }
 
-    # Dataset EE -> robot joints
-    joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+        # Get robot observation
+        robot_obs = robot.get_observation()
 
-    # Send action to robot
-    _ = robot.send_action(joint_action)
+        # Dataset EE -> robot joints
+        joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
 
-    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
+        # Send action to robot
+        _ = robot.send_action(joint_action)
 
-# Clean up
-robot.disconnect()
+        precise_sleep(1.0 / dataset.fps - (time.perf_counter() - t0))
+
+    # Clean up
+    robot.disconnect()
+
+
+if __name__ == "__main__":
+    main()

examples/phone_to_so100/teleoperate.py

@@ -32,82 +32,90 @@ 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 busy_wait
+from lerobot.utils.robot_utils import precise_sleep
 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
 
-# Initialize the robot and teleoperator
-robot = SO100Follower(robot_config)
-teleop_device = Phone(teleop_config)
+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
 
-# 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()),
-)
+    # Initialize the robot and teleoperator
+    robot = SO100Follower(robot_config)
+    teleop_device = Phone(teleop_config)
 
-# 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,
-)
+    # 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()),
+    )
 
-# Connect to the robot and teleoperator
-robot.connect()
-teleop_device.connect()
+    # 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,
+    )
 
-# Init rerun viewer
-init_rerun(session_name="phone_so100_teleop")
+    # Connect to the robot and teleoperator
+    robot.connect()
+    teleop_device.connect()
 
-if not robot.is_connected or not teleop_device.is_connected:
-    raise ValueError("Robot or teleop is not connected!")
+    # Init rerun viewer
+    init_rerun(session_name="phone_so100_teleop")
 
-print("Starting teleop loop. Move your phone to teleoperate the robot...")
-while True:
-    t0 = time.perf_counter()
+    if not robot.is_connected or not teleop_device.is_connected:
+        raise ValueError("Robot or teleop is not connected!")
 
-    # Get robot observation
-    robot_obs = robot.get_observation()
+    print("Starting teleop loop. Move your phone to teleoperate the robot...")
+    while True:
+        t0 = time.perf_counter()
 
-    # Get teleop action
-    phone_obs = teleop_device.get_action()
+        # Get robot observation
+        robot_obs = robot.get_observation()
 
-    # Phone -> EE pose -> Joints transition
-    joint_action = phone_to_robot_joints_processor((phone_obs, robot_obs))
+        # Get teleop action
+        phone_obs = teleop_device.get_action()
 
-    # Send action to robot
-    _ = robot.send_action(joint_action)
+        # Phone -> EE pose -> Joints transition
+        joint_action = phone_to_robot_joints_processor((phone_obs, robot_obs))
 
-    # Visualize
-    log_rerun_data(observation=phone_obs, action=joint_action)
+        # Send action to robot
+        _ = robot.send_action(joint_action)
 
-    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+        # 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()

examples/so100_to_so100_EE/evaluate.py

@@ -52,126 +52,114 @@ 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,
-)
 
-robot = SO100Follower(robot_config)
-
-# 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()),
-)
-
-# 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,
-        ),
-    ),
-    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,
+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,
     )
 
-    # 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")
+    robot = SO100Follower(robot_config)
+
+    # 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()),
+    )
+
+    # 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,
+            ),
+        ),
+        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,
@@ -180,21 +168,40 @@ for episode_idx in range(NUM_EPISODES):
             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
+        # 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,
+            )
 
-    # Save episode
-    dataset.save_episode()
-    episode_idx += 1
+        if events["rerecord_episode"]:
+            log_say("Re-record episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue
 
-# Clean up
-log_say("Stop recording")
-robot.disconnect()
-listener.stop()
+        # Save episode
+        dataset.save_episode()
+        episode_idx += 1
 
-dataset.finalize()
-dataset.push_to_hub()
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    listener.stop()
+
+    dataset.finalize()
+    dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/so100_to_so100_EE/record.py

@@ -48,134 +48,122 @@ 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")
 
-# Initialize the robot and teleoperator
-follower = SO100Follower(follower_config)
-leader = SO100Leader(leader_config)
+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")
 
-# 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()),
-)
+    # 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
-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,
-        ),
-    ),
-    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,
+    # 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()),
     )
 
-    # 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")
+    # 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,
+            ),
+        ),
+        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,
-            control_time_s=RESET_TIME_SEC,
+            dataset=dataset,
+            control_time_s=EPISODE_TIME_SEC,
             single_task=TASK_DESCRIPTION,
             display_data=True,
             teleop_action_processor=leader_joints_to_ee,
@@ -183,22 +171,42 @@ while episode_idx < NUM_EPISODES and not events["stop_recording"]:
             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
+        # 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,
+            )
 
-    # Save episode
-    dataset.save_episode()
-    episode_idx += 1
+        if events["rerecord_episode"]:
+            log_say("Re-recording episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue
 
-# Clean up
-log_say("Stop recording")
-leader.disconnect()
-follower.disconnect()
-listener.stop()
+        # Save episode
+        dataset.save_episode()
+        episode_idx += 1
 
-dataset.finalize()
-dataset.push_to_hub()
+    # Clean up
+    log_say("Stop recording")
+    leader.disconnect()
+    follower.disconnect()
+    listener.stop()
+
+    dataset.finalize()
+    dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/so100_to_so100_EE/replay.py

@@ -30,72 +30,78 @@ 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 busy_wait
+from lerobot.utils.robot_utils import precise_sleep
 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
-)
 
-# Initialize the robot
-robot = SO100Follower(robot_config)
+def main():
+    # Initialize the robot config
+    robot_config = SO100FollowerConfig(
+        port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
+    )
 
-# 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()),
-)
+    # Initialize the robot
+    robot = SO100Follower(robot_config)
 
-# 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,
-)
+    # 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()),
+    )
 
-# 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)
+    # 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,
+    )
 
-# Connect to the robot
-robot.connect()
+    # 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)
 
-if not robot.is_connected:
-    raise ValueError("Robot is not connected!")
+    # Connect to the robot
+    robot.connect()
 
-print("Starting replay loop...")
-log_say(f"Replaying episode {EPISODE_IDX}")
-for idx in range(len(episode_frames)):
-    t0 = time.perf_counter()
+    if not robot.is_connected:
+        raise ValueError("Robot is not connected!")
 
-    # Get recorded action from dataset
-    ee_action = {
-        name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-    }
+    print("Starting replay loop...")
+    log_say(f"Replaying episode {EPISODE_IDX}")
+    for idx in range(len(episode_frames)):
+        t0 = time.perf_counter()
 
-    # Get robot observation
-    robot_obs = robot.get_observation()
+        # Get recorded action from dataset
+        ee_action = {
+            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
+        }
 
-    # Dataset EE -> robot joints
-    joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+        # Get robot observation
+        robot_obs = robot.get_observation()
 
-    # Send action to robot
-    _ = robot.send_action(joint_action)
+        # Dataset EE -> robot joints
+        joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
 
-    busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
+        # Send action to robot
+        _ = robot.send_action(joint_action)
 
-# Clean up
-robot.disconnect()
+        precise_sleep(1.0 / dataset.fps - (time.perf_counter() - t0))
+
+    # Clean up
+    robot.disconnect()
+
+
+if __name__ == "__main__":
+    main()

examples/so100_to_so100_EE/teleoperate.py

@@ -32,90 +32,96 @@ 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 busy_wait
+from lerobot.utils.robot_utils import precise_sleep
 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")
 
-# Initialize the robot and teleoperator
-follower = SO100Follower(follower_config)
-leader = SO100Leader(leader_config)
+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")
 
-# 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()),
-)
+    # 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
-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
+    follower_kinematics_solver = RobotKinematics(
+        urdf_path="./SO101/so101_new_calib.urdf",
+        target_frame_name="gripper_frame_link",
+        joint_names=list(follower.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,
-)
+    # 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 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 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,
+    )
 
-# Connect to the robot and teleoperator
-follower.connect()
-leader.connect()
+    # 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,
+    )
 
-# Init rerun viewer
-init_rerun(session_name="so100_so100_EE_teleop")
+    # Connect to the robot and teleoperator
+    follower.connect()
+    leader.connect()
 
-print("Starting teleop loop...")
-while True:
-    t0 = time.perf_counter()
+    # Init rerun viewer
+    init_rerun(session_name="so100_so100_EE_teleop")
 
-    # Get robot observation
-    robot_obs = follower.get_observation()
+    print("Starting teleop loop...")
+    while True:
+        t0 = time.perf_counter()
 
-    # Get teleop observation
-    leader_joints_obs = leader.get_action()
+        # Get robot observation
+        robot_obs = follower.get_observation()
 
-    # teleop joints -> teleop EE action
-    leader_ee_act = leader_to_ee(leader_joints_obs)
+        # Get teleop observation
+        leader_joints_obs = leader.get_action()
 
-    # teleop EE -> robot joints
-    follower_joints_act = ee_to_follower_joints((leader_ee_act, robot_obs))
+        # teleop joints -> teleop EE action
+        leader_ee_act = leader_to_ee(leader_joints_obs)
 
-    # Send action to robot
-    _ = follower.send_action(follower_joints_act)
+        # teleop EE -> robot joints
+        follower_joints_act = ee_to_follower_joints((leader_ee_act, robot_obs))
 
-    # Visualize
-    log_rerun_data(observation=leader_ee_act, action=follower_joints_act)
+        # Send action to robot
+        _ = follower.send_action(follower_joints_act)
 
-    busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+        # 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()

examples/tutorial/act/act_training_example.py

@@ -19,80 +19,86 @@ def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[flo
     return [i / fps for i in delta_indices]
 
 
-output_directory = Path("outputs/robot_learning_tutorial/act")
-output_directory.mkdir(parents=True, exist_ok=True)
+def main():
+    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("fracapuano/robot_learning_tutorial_act")
-preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")
-postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")
+    # 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()

examples/tutorial/act/act_using_example.py

@@ -8,50 +8,56 @@ 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),
-}
 
-robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-robot = SO100Follower(robot_cfg)
-robot.connect()
+def main():
+    device = torch.device("mps")  # or "cuda" or "cpu"
+    model_id = "<user>/robot_learning_tutorial_act"
+    model = ACTPolicy.from_pretrained(model_id)
 
-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
-        )
+    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)
 
-        obs = preprocess(obs_frame)
+    # # find ports using lerobot-find-port
+    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
-        action = model.select_action(obs)
-        action = postprocess(action)
+    # # the robot ids are used the load the right calibration files
+    follower_id = ...  # something like "follower_so100"
 
-        action = make_robot_action(action, dataset_metadata.features)
+    # 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.send_action(action)
+    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+    robot = SO100Follower(robot_cfg)
+    robot.connect()
 
-    print("Episode finished! Starting new episode...")
+    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()

examples/tutorial/async-inf/policy_server.py

@@ -1,11 +1,17 @@
 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
 
-config = PolicyServerConfig(
-    host=host,
-    port=port,
-)
-serve(config)
+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()

examples/tutorial/async-inf/robot_client.py

@@ -6,50 +6,56 @@ 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),
-}
 
-# # find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+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),
+    }
 
-# # the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
+    # # find ports using lerobot-find-port
+    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
-robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_cfg)
+    # # the robot ids are used the load the right calibration files
+    follower_id = ...  # something like "follower_so100"
 
-server_address = ...  # something like "127.0.0.1:8080" if using localhost
+    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_cfg)
 
-# 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
-)
+    server_address = ...  # something like "127.0.0.1:8080" if using localhost
 
-# 4. Create and start client
-client = RobotClient(client_cfg)
+    # 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
+    )
 
-# 5. Provide a textual description of the task
-task = ...
+    # 4. Create and start client
+    client = RobotClient(client_cfg)
 
-if client.start():
-    # Start action receiver thread
-    action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
-    action_receiver_thread.start()
+    # 5. Provide a textual description of the task
+    task = ...
 
-    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 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()

examples/tutorial/diffusion/diffusion_training_example.py

@@ -19,81 +19,87 @@ def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[flo
     return [i / fps for i in delta_indices]
 
 
-output_directory = Path("outputs/robot_learning_tutorial/diffusion")
-output_directory.mkdir(parents=True, exist_ok=True)
+def main():
+    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("fracapuano/robot_learning_tutorial_diffusion")
-preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
-postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
+    # 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()

examples/tutorial/diffusion/diffusion_using_example.py

@@ -8,53 +8,57 @@ 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
 
 
-# # find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+def main():
+    device = torch.device("mps")  # or "cuda" or "cpu"
+    model_id = "<user>/robot_learning_tutorial_diffusion"
 
-# # the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
+    model = DiffusionPolicy.from_pretrained(model_id)
 
-# 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),
-}
+    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_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
-robot = SO100Follower(robot_cfg)
-robot.connect()
+    # # 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...")
 
 
-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()

examples/tutorial/pi0/using_pi0_example.py

@@ -11,57 +11,63 @@ 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"
 
-model = PI0Policy.from_pretrained(model_id)
+def main():
+    device = torch.device("mps")  # or "cuda" or "cpu"
+    model_id = "lerobot/pi0_base"
 
-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)}},
-)
+    model = PI0Policy.from_pretrained(model_id)
 
-# find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+    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)}},
+    )
 
-# the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
+    # find ports using lerobot-find-port
+    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
-# 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),
-}
+    # 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_config)
-robot = SO100Follower(robot_cfg)
-robot.connect()
+    # 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),
+    }
 
-task = ""  # something like "pick the red block"
-robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
+    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+    robot = SO100Follower(robot_cfg)
+    robot.connect()
 
-# 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}
+    task = ""  # something like "pick the red block"
+    robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
 
-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
-        )
+    # 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}
 
-        obs = preprocess(obs_frame)
+    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
+            )
 
-        action = model.select_action(obs)
-        action = postprocess(action)
-        action = make_robot_action(action, dataset_features)
-        robot.send_action(action)
+            obs = preprocess(obs_frame)
 
-    print("Episode finished! Starting new episode...")
+            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()

examples/tutorial/rl/hilserl_example.py

@@ -20,6 +20,8 @@ from lerobot.teleoperators.utils import TeleopEvents
 
 LOG_EVERY = 10
 SEND_EVERY = 10
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
 
 
 def run_learner(
@@ -223,123 +225,123 @@ def make_policy_obs(obs, device: torch.device = "cpu"):
     }
 
 
-"""Main function - coordinates actor and learner processes."""
+def main():
+    """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 = "fracapuano/reward_classifier_hil_serl_example"
-reward_classifier = Classifier.from_pretrained(reward_classifier_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)
 
-reward_classifier.to(device)
-reward_classifier.eval()
+    reward_classifier.to(device)
+    reward_classifier.eval()
 
-MAX_EPISODES = 5
-MAX_STEPS_PER_EPISODE = 20
+    # 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")
 
-# 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")
+    env_cfg = HILSerlRobotEnvConfig(robot=robot_cfg, teleop=teleop_cfg, processor=processor_cfg)
 
-env_cfg = HILSerlRobotEnvConfig(robot=robot_cfg, teleop=teleop_cfg, processor=processor_cfg)
+    # Create robot environment
+    env, teleop_device = make_robot_env(env_cfg)
 
-# Create robot environment
-env, teleop_device = make_robot_env(env_cfg)
+    obs_features = hw_to_dataset_features(env.robot.observation_features, "observation")
+    action_features = hw_to_dataset_features(env.robot.action_features, "action")
 
-obs_features = hw_to_dataset_features(env.robot.observation_features, "observation")
-action_features = hw_to_dataset_features(env.robot.action_features, "action")
+    # Create SAC policy for action selection
+    policy_cfg = SACConfig(
+        device=device,
+        input_features=obs_features,
+        output_features=action_features,
+    )
 
-# Create SAC policy for action selection
-policy_cfg = SACConfig(
-    device=device,
-    input_features=obs_features,
-    output_features=action_features,
-)
+    policy_actor = SACPolicy(policy_cfg)
+    policy_learner = SACPolicy(policy_cfg)
 
-policy_actor = SACPolicy(policy_cfg)
-policy_learner = SACPolicy(policy_cfg)
+    demonstrations_repo_id = "lerobot/example_hil_serl_dataset"
+    offline_dataset = LeRobotDataset(repo_id=demonstrations_repo_id)
 
-demonstrations_repo_id = "lerobot/example_hil_serl_dataset"
-offline_dataset = LeRobotDataset(repo_id=demonstrations_repo_id)
+    # 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())
+    )
 
-# 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())
-)
+    # Create communication channels between learner and actor processes
+    transitions_queue = mp.Queue(maxsize=10)
+    parameters_queue = mp.Queue(maxsize=2)
+    shutdown_event = mp.Event()
 
-# Create communication channels between learner and actor processes
-transitions_queue = mp.Queue(maxsize=10)
-parameters_queue = mp.Queue(maxsize=2)
-shutdown_event = mp.Event()
+    # 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()
 
 
-# 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()
+if __name__ == "__main__":
+    main()

examples/tutorial/rl/reward_classifier_example.py

@@ -4,59 +4,64 @@ 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"
 
-# Load the dataset used for training
-repo_id = "lerobot/example_hil_serl_dataset"
-dataset = LeRobotDataset(repo_id)
+def main():
+    # Device to use for training
+    device = "mps"  # or "cuda", or "cpu"
 
-# Configure the policy to extract features from the image frames
-camera_keys = dataset.meta.camera_keys
+    # Load the dataset used for training
+    repo_id = "lerobot/example_hil_serl_dataset"
+    dataset = LeRobotDataset(repo_id)
 
-config = RewardClassifierConfig(
-    num_cameras=len(camera_keys),
-    device=device,
-    # backbone model to extract features from the image frames
-    model_name="microsoft/resnet-18",
-)
+    # Configure the policy to extract features from the image frames
+    camera_keys = dataset.meta.camera_keys
 
-# 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)
+    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)
 
 
-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)
+if __name__ == "__main__":
+    main()

examples/tutorial/smolvla/using_smolvla_example.py

@@ -11,56 +11,62 @@ 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"
 
-model = SmolVLAPolicy.from_pretrained(model_id)
+def main():
+    device = torch.device("mps")  # or "cuda" or "cpu"
+    model_id = "lerobot/smolvla_base"
 
-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)}},
-)
+    model = SmolVLAPolicy.from_pretrained(model_id)
 
-# find ports using lerobot-find-port
-follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+    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)}},
+    )
 
-# the robot ids are used the load the right calibration files
-follower_id = ...  # something like "follower_so100"
+    # find ports using lerobot-find-port
+    follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
 
-# 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),
-}
+    # 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_config)
-robot = SO100Follower(robot_cfg)
-robot.connect()
+    # 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),
+    }
 
-task = ""  # something like "pick the red block"
-robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
+    robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+    robot = SO100Follower(robot_cfg)
+    robot.connect()
 
-# 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}
+    task = ""  # something like "pick the red block"
+    robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
 
-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
-        )
+    # 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}
 
-        obs = preprocess(obs_frame)
+    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
+            )
 
-        action = model.select_action(obs)
-        action = postprocess(action)
-        action = make_robot_action(action, dataset_features)
-        robot.send_action(action)
+            obs = preprocess(obs_frame)
 
-    print("Episode finished! Starting new episode...")
+            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()

examples/unitree_g1/gr00t_locomotion.py

@@ -0,0 +1,347 @@
+#!/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!")

loop_datasets.py

@@ -0,0 +1,10 @@
+from huggingface_hub import HfApi, list_datasets
+
+api = HfApi()
+datasets = list_datasets(author="lerobot-data-collection")
+print('"[', end="")
+i=0
+for dataset in datasets:
+    if "three-folds-dataset" in dataset.id:
+        print("'" + dataset.id + "',", end="")
+print(']"',)

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.4.2"
+version = "0.4.3"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 readme = "README.md"
 license = { text = "Apache-2.0" }
@@ -102,11 +102,17 @@ grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"] # TODO: Bumb dependency (com
 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
 dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"]
+damiao = ["python-can>=4.2.0,<5.0.0"]
 
 # Robots
+openarms = ["lerobot[damiao]"]
 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 = [
@@ -129,6 +135,7 @@ 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
@@ -148,6 +155,7 @@ metaworld = ["metaworld==3.0.0"]
 # All
 all = [
     "lerobot[dynamixel]",
+    "lerobot[openarms]",
     "lerobot[gamepad]",
     "lerobot[hopejr]",
     "lerobot[lekiwi]",
@@ -157,6 +165,7 @@ 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]",
@@ -257,6 +266,7 @@ default.extend-ignore-identifiers-re = [
     "ein",
     "thw",
     "inpt",
+    "ROBOTIS",
 ]
 
 # TODO: Uncomment when ready to use
@@ -356,9 +366,9 @@ 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.*"

src/lerobot/async_inference/constants.py

@@ -26,4 +26,4 @@ DEFAULT_OBS_QUEUE_TIMEOUT = 2
 SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "tdmpc", "vqbet", "pi0", "pi05"]
 
 # TODO: Add all other robots
-SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so100_follower"]
+SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so100_follower", "omx_follower"]

src/lerobot/async_inference/robot_client.py

@@ -54,6 +54,7 @@ from lerobot.robots import (  # noqa: F401
     bi_so100_follower,
     koch_follower,
     make_robot_from_config,
+    omx_follower,
     so100_follower,
     so101_follower,
 )

src/lerobot/configs/train.py

@@ -56,6 +56,7 @@ class TrainPipelineConfig(HubMixin):
     steps: int = 100_000
     eval_freq: int = 20_000
     log_freq: int = 200
+    tolerance_s: float = 1e-4
     save_checkpoint: bool = True
     # Checkpoint is saved every `save_freq` training iterations and after the last training step.
     save_freq: int = 20_000

src/lerobot/datasets/aggregate.py

@@ -136,21 +136,40 @@ def update_meta_data(
         df["_orig_chunk"] = df[orig_chunk_col].copy()
         df["_orig_file"] = df[orig_file_col].copy()
 
-        # 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
+        # Get mappings for this video key
         src_to_offset = video_idx.get("src_to_offset", {})
-        if src_to_offset:
-            # Apply offset based on original source file
+        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
             for idx in df.index:
-                src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"])
+                # 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"]))
                 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"]
             )
@@ -268,6 +287,12 @@ 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 = {
@@ -282,9 +307,13 @@ 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"]
-        current_offset = video_idx["latest_duration"]
+        dst_file_durations = video_idx["dst_file_durations"]
 
         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,
@@ -298,14 +327,17 @@ 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():
-                # Store offset before incrementing
-                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset
+                # 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
                 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
@@ -313,10 +345,11 @@ 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, 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
+                # 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)
+                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
                 dst_path = dst_meta.root / DEFAULT_VIDEO_PATH.format(
                     video_key=key,
                     chunk_index=chunk_idx,
@@ -324,16 +357,20 @@ 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))
-                # Reset offset for next file
-                current_offset = src_duration
+                # Track duration of this new destination file
+                dst_file_durations[dst_key] = src_duration
             else:
-                # Append to existing video file - use current accumulated offset
-                videos_idx[key]["src_to_offset"][(src_chunk_idx, src_file_idx)] = current_offset
+                # 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
                 concatenate_video_files(
                     [dst_path, src_path],
                     dst_path,
                 )
-                current_offset += src_duration
+                # Update duration of this destination file
+                dst_file_durations[dst_key] = current_dst_duration + src_duration
 
             videos_idx[key]["episode_duration"] += src_duration
 

src/lerobot/datasets/factory.py

@@ -98,6 +98,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                 image_transforms=image_transforms,
                 revision=cfg.dataset.revision,
                 video_backend=cfg.dataset.video_backend,
+                tolerance_s=cfg.tolerance_s,
             )
         else:
             dataset = StreamingLeRobotDataset(
@@ -108,6 +109,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                 image_transforms=image_transforms,
                 revision=cfg.dataset.revision,
                 max_num_shards=cfg.num_workers,
+                tolerance_s=cfg.tolerance_s,
             )
     else:
         raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")

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 = item
-        write_image(image_array, fpath)
+        image_array, fpath, compress_level = item
+        write_image(image_array, fpath, compress_level)
         queue.task_done()
 
 
@@ -169,11 +169,13 @@ class AsyncImageWriter:
                 p.start()
                 self.processes.append(p)
 
-    def save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path):
+    def save_image(
+        self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1
+    ):
         if isinstance(image, torch.Tensor):
             # Convert tensor to numpy array to minimize main process time
             image = image.cpu().numpy()
-        self.queue.put((image, fpath))
+        self.queue.put((image, fpath, compress_level))
 
     def wait_until_done(self):
         self.queue.join()

src/lerobot/datasets/lerobot_dataset.py

@@ -13,6 +13,7 @@
 # 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
@@ -22,11 +23,13 @@ from pathlib import Path
 
 import datasets
 import numpy as np
+import os
 import packaging.version
 import pandas as pd
 import PIL.Image
 import pyarrow as pa
 import pyarrow.parquet as pq
+from concurrent.futures import ProcessPoolExecutor
 import torch
 import torch.utils
 from huggingface_hub import HfApi, snapshot_download
@@ -539,6 +542,15 @@ 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,
@@ -1071,6 +1083,7 @@ 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
@@ -1080,13 +1093,15 @@ 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) -> None:
+    def _save_image(
+        self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1
+    ) -> None:
         if self.image_writer is None:
             if isinstance(image, torch.Tensor):
                 image = image.cpu().numpy()
-            write_image(image, fpath)
+            write_image(image, fpath, compress_level=compress_level)
         else:
-            self.image_writer.save_image(image=image, fpath=fpath)
+            self.image_writer.save_image(image=image, fpath=fpath, compress_level=compress_level)
 
     def add_frame(self, frame: dict) -> None:
         """
@@ -1124,14 +1139,19 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 )
                 if frame_index == 0:
                     img_path.parent.mkdir(parents=True, exist_ok=True)
-                self._save_image(frame[key], img_path)
+                compress_level = 1 if self.features[key]["dtype"] == "video" else 6
+                self._save_image(frame[key], img_path, compress_level)
                 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) -> None:
+    def save_episode(
+        self,
+        episode_data: dict | None = None,
+        parallel_encoding: bool = True,
+    ) -> None:
         """
         This will save to disk the current episode in self.episode_buffer.
 
@@ -1143,6 +1163,8 @@ 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
 
@@ -1179,8 +1201,43 @@ class LeRobotDataset(torch.utils.data.Dataset):
         use_batched_encoding = self.batch_encoding_size > 1
 
         if has_video_keys and not use_batched_encoding:
-            for video_key in self.meta.video_keys:
-                ep_metadata.update(self._save_episode_video(video_key, episode_index))
+            video_paths = self._encode_multiple_temporary_episode_videos(self.meta.video_keys, episode_index)
+            for (video_key, video_path) in zip(self.meta.video_keys, video_paths):
+                ep_metadata.update(self._save_episode_video(video_key, episode_index, video_path))
+            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))
 
         # `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)
@@ -1345,9 +1402,18 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         return metadata
 
-    def _save_episode_video(self, video_key: str, episode_index: int) -> dict:
+    def _save_episode_video(
+        self,
+        video_key: str,
+        episode_index: int,
+        temp_path: Path | None = None,
+    ) -> dict:
         # Encode episode frames into a temporary video
-        ep_path = self._encode_temporary_episode_video(video_key, episode_index)
+        if temp_path is None:
+            ep_path = self._encode_temporary_episode_video(video_key, episode_index)
+        else:
+            ep_path = temp_path
+
         ep_size_in_mb = get_file_size_in_mb(ep_path)
         ep_duration_in_s = get_video_duration_in_s(ep_path)
 
@@ -1465,11 +1531,23 @@ 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.
         """
-        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
+        return _encode_video_worker(video_key, episode_index, self.root, self.fps)
+
+    def _encode_multiple_temporary_episode_videos(self, video_keys, episode_index):
+        temp_paths = []
+        img_dirs = []
+        for video_key in video_keys:
+            temp_paths.append(Path(tempfile.mkdtemp(dir=self.root)) / f"{video_key}_{episode_index:03d}.mp4")
+            img_dirs.append(self._get_image_file_dir(episode_index, video_key))
+        fps = [self.fps]*len(video_keys)
+
+        with ProcessPoolExecutor(max_workers=len(video_keys)) as executor:
+            executor.map(encode_video_frames,img_dirs,temp_paths,fps)
+
+        for img_dir in img_dirs:
+            shutil.rmtree(img_dir)
+
+        return temp_paths
 
     @classmethod
     def create(

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 = 500  # Max size per file
+DEFAULT_VIDEO_FILE_SIZE_IN_MB = 200  # Max size per file
 
 INFO_PATH = "meta/info.json"
 STATS_PATH = "meta/stats.json"

src/lerobot/datasets/video_utils.py

@@ -310,7 +310,8 @@ def encode_video_frames(
     crf: int | None = 30,
     fast_decode: int = 0,
     log_level: int | None = av.logging.ERROR,
-    overwrite: bool = False,
+    overwrite: bool = True,
+    preset: int | None = None,
 ) -> None:
     """More info on ffmpeg arguments tuning on `benchmark/video/README.md`"""
     # Check encoder availability
@@ -354,11 +355,17 @@ def encode_video_frames(
     if crf is not None:
         video_options["crf"] = str(crf)
 
+    #TEMPORARY FIX
+    video_options["preset"] = "12"
+
     if fast_decode:
         key = "svtav1-params" if vcodec == "libsvtav1" else "tune"
         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 = 520
+    episode_length: int | None = None
     obs_type: str = "pixels_agent_pos"
     render_mode: str = "rgb_array"
     camera_name: str = "agentview_image,robot0_eye_in_hand_image"
@@ -272,6 +272,7 @@ 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

@@ -19,8 +19,10 @@ 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
@@ -39,6 +41,7 @@ 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]],
@@ -61,6 +64,10 @@ 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:
@@ -136,6 +143,8 @@ 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

@@ -80,10 +80,7 @@ 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] = {
@@ -103,6 +100,7 @@ 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",
@@ -114,6 +112,7 @@ 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
@@ -141,14 +140,19 @@ 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)
-
+        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
         images = {}
         for cam in self.camera_name:
             images[self.camera_name_mapping[cam]] = spaces.Box(
@@ -296,6 +300,15 @@ 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
@@ -341,8 +354,10 @@ 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)."""
 
@@ -354,7 +369,9 @@ 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,
         )
 
@@ -374,6 +391,8 @@ 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.
@@ -415,12 +434,14 @@ 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/motors/__init__.py

@@ -14,4 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .motors_bus import Motor, MotorCalibration, MotorNormMode, MotorsBus
+from .motors_bus import (
+    Motor,
+    MotorCalibration,
+    MotorNormMode,
+    MotorsBus,  # Backward compatibility (alias for SerialMotorsBus)
+    MotorsBusBase,
+    SerialMotorsBus,
+)

src/lerobot/motors/damiao/__init__.py

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

src/lerobot/motors/damiao/damiao.py

@@ -0,0 +1,905 @@
+# 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.
+
+# TODO(pepijn): add license of: https://github.com/cmjang/DM_Control_Python?tab=MIT-1-ov-file#readme
+
+import logging
+import time
+from contextlib import contextmanager
+from copy import deepcopy
+from functools import cached_property
+from typing import Dict, List, Optional, Tuple, Union
+
+import can
+import numpy as np
+
+from lerobot.motors import Motor, MotorCalibration, MotorNormMode, MotorsBusBase
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.utils import enter_pressed, move_cursor_up
+
+from .tables import (
+    AVAILABLE_BAUDRATES,
+    CAN_CMD_DISABLE,
+    CAN_CMD_ENABLE,
+    CAN_CMD_REFRESH,
+    CAN_CMD_SET_ZERO,
+    CAN_PARAM_ID,
+    DEFAULT_BAUDRATE,
+    DEFAULT_TIMEOUT_MS,
+    MODEL_RESOLUTION,
+    MOTOR_LIMIT_PARAMS,
+    NORMALIZED_DATA,
+    MotorType,
+)
+
+logger = logging.getLogger(__name__)
+
+NameOrID = Union[str, int]
+Value = Union[int, float]
+
+
+class DamiaoMotorsBus(MotorsBusBase):
+    """
+    The Damiao implementation for a MotorsBus using CAN bus communication.
+    
+    This class uses python-can for CAN bus communication with Damiao motors.
+    For more info, see:
+    - python-can documentation: https://python-can.readthedocs.io/en/stable/
+    - Seedstudio documentation: https://wiki.seeedstudio.com/damiao_series/
+    - DM_Control_Python repo: https://github.com/cmjang/DM_Control_Python
+    """
+
+    # CAN-specific settings
+    available_baudrates = deepcopy(AVAILABLE_BAUDRATES)
+    default_baudrate = DEFAULT_BAUDRATE
+    default_timeout = DEFAULT_TIMEOUT_MS
+    
+    # Motor configuration
+    model_resolution_table = deepcopy(MODEL_RESOLUTION)
+    normalized_data = deepcopy(NORMALIZED_DATA)
+
+    def __init__(
+        self,
+        port: str,
+        motors: dict[str, Motor],
+        calibration: dict[str, MotorCalibration] | None = None,
+        can_interface: str = "auto",
+        use_can_fd: bool = True,
+        bitrate: int = 1000000,
+        data_bitrate: int | None = 5000000,
+    ):
+        """
+        Initialize the Damiao motors bus.
+
+        Args:
+            port: CAN interface name (e.g., "can0" for Linux, "/dev/cu.usbmodem*" for macOS)
+            motors: Dictionary mapping motor names to Motor objects
+            calibration: Optional calibration data
+            can_interface: CAN interface type - "auto" (default), "socketcan" (Linux), or "slcan" (macOS/serial)
+            use_can_fd: Whether to use CAN FD mode (default: True for OpenArms)
+            bitrate: Nominal bitrate in bps (default: 1000000 = 1 Mbps)
+            data_bitrate: Data bitrate for CAN FD in bps (default: 5000000 = 5 Mbps), ignored if use_can_fd is False
+        """
+        super().__init__(port, motors, calibration)
+        self.port = port
+        self.can_interface = can_interface
+        self.use_can_fd = use_can_fd
+        self.bitrate = bitrate
+        self.data_bitrate = data_bitrate
+        self.canbus = None
+        self._is_connected = False
+        
+        # Map motor names to CAN IDs
+        self._motor_can_ids = {}
+        self._recv_id_to_motor = {}
+        
+        # Store motor types and recv IDs
+        self._motor_types = {}
+        for name, motor in self.motors.items():
+            if hasattr(motor, "motor_type"):
+                self._motor_types[name] = motor.motor_type
+            else:
+                # Default to DM4310 if not specified
+                self._motor_types[name] = MotorType.DM4310
+            
+            # Map recv_id to motor name for filtering responses
+            if hasattr(motor, "recv_id"):
+                self._recv_id_to_motor[motor.recv_id] = name
+
+    @property
+    def is_connected(self) -> bool:
+        """Check if the CAN bus is connected."""
+        return self._is_connected and self.canbus is not None
+
+    def connect(self, handshake: bool = True) -> None:
+        """
+        Open the CAN bus and initialize communication.
+
+        Args:
+            handshake: If True, ping all motors to verify they're present
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(
+                f"{self.__class__.__name__}('{self.port}') is already connected."
+            )
+
+        try:
+            # Auto-detect interface type based on port name
+            if self.can_interface == "auto":
+                if self.port.startswith("/dev/"):
+                    # Serial device (macOS/Windows)
+                    self.can_interface = "slcan"
+                    logger.info(f"Auto-detected slcan interface for port {self.port}")
+                else:
+                    # Network interface (Linux)
+                    self.can_interface = "socketcan"
+                    logger.info(f"Auto-detected socketcan interface for port {self.port}")
+            
+            # Connect to CAN bus
+            if self.can_interface == "socketcan":
+                # Linux SocketCAN with CAN FD support
+                if self.use_can_fd and self.data_bitrate is not None:
+                    self.canbus = can.interface.Bus(
+                        channel=self.port,
+                        interface="socketcan",
+                        bitrate=self.bitrate,
+                        data_bitrate=self.data_bitrate,
+                        fd=True
+                    )
+                    logger.info(f"Connected to {self.port} with CAN FD (bitrate={self.bitrate}, data_bitrate={self.data_bitrate})")
+                else:
+                    self.canbus = can.interface.Bus(
+                        channel=self.port,
+                        interface="socketcan",
+                        bitrate=self.bitrate
+                    )
+                    logger.info(f"Connected to {self.port} with CAN 2.0 (bitrate={self.bitrate})")
+            elif self.can_interface == "slcan":
+                # Serial Line CAN (macOS, Windows, or USB adapters)
+                # Note: SLCAN typically doesn't support CAN FD
+                self.canbus = can.interface.Bus(
+                    channel=self.port,
+                    interface="slcan",
+                    bitrate=self.bitrate
+                )
+                logger.info(f"Connected to {self.port} with SLCAN (bitrate={self.bitrate})")
+            else:
+                # Generic interface (vector, pcan, etc.)
+                if self.use_can_fd and self.data_bitrate is not None:
+                    self.canbus = can.interface.Bus(
+                        channel=self.port,
+                        interface=self.can_interface,
+                        bitrate=self.bitrate,
+                        data_bitrate=self.data_bitrate,
+                        fd=True
+                    )
+                else:
+                    self.canbus = can.interface.Bus(
+                        channel=self.port,
+                        interface=self.can_interface,
+                        bitrate=self.bitrate
+                    )
+            
+            self._is_connected = True
+            
+            if handshake:
+                self._handshake()
+                
+            logger.debug(f"{self.__class__.__name__} connected via {self.can_interface}.")
+        except Exception as e:
+            self._is_connected = False
+            raise ConnectionError(f"Failed to connect to CAN bus: {e}")
+
+    def _handshake(self) -> None:
+        """Verify all motors are present by refreshing their status."""
+        for motor_name in self.motors:
+            self._refresh_motor(motor_name)
+            time.sleep(0.01)  # Small delay between motors
+
+    def disconnect(self, disable_torque: bool = True) -> None:
+        """
+        Close the CAN bus connection.
+
+        Args:
+            disable_torque: If True, disable torque on all motors before disconnecting
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(
+                f"{self.__class__.__name__}('{self.port}') is not connected."
+            )
+
+        if disable_torque:
+            try:
+                self.disable_torque()
+            except Exception as e:
+                logger.warning(f"Failed to disable torque during disconnect: {e}")
+
+        if self.canbus:
+            self.canbus.shutdown()
+            self.canbus = None
+        self._is_connected = False
+        logger.debug(f"{self.__class__.__name__} disconnected.")
+
+    def configure_motors(self) -> None:
+        """Configure all motors with default settings."""
+        # Damiao motors don't require much configuration in MIT mode
+        # Just ensure they're enabled
+        for motor in self.motors:
+            self._enable_motor(motor)
+            time.sleep(0.01)
+
+    def _enable_motor(self, motor: NameOrID) -> None:
+        """Enable a single motor."""
+        motor_id = self._get_motor_id(motor)
+        recv_id = self._get_motor_recv_id(motor)
+        data = [0xFF] * 7 + [CAN_CMD_ENABLE]
+        msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+        self.canbus.send(msg)
+        self._recv_motor_response(expected_recv_id=recv_id)
+
+    def _disable_motor(self, motor: NameOrID) -> None:
+        """Disable a single motor."""
+        motor_id = self._get_motor_id(motor)
+        recv_id = self._get_motor_recv_id(motor)
+        data = [0xFF] * 7 + [CAN_CMD_DISABLE]
+        msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+        self.canbus.send(msg)
+        self._recv_motor_response(expected_recv_id=recv_id)
+
+    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+        """Enable torque on selected motors."""
+        motors = self._get_motors_list(motors)
+        for motor in motors:
+            for _ in range(num_retry + 1):
+                try:
+                    self._enable_motor(motor)
+                    break
+                except Exception as e:
+                    if _ == num_retry:
+                        raise e
+                    time.sleep(0.01)
+
+    def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+        """Disable torque on selected motors."""
+        motors = self._get_motors_list(motors)
+        for motor in motors:
+            for _ in range(num_retry + 1):
+                try:
+                    self._disable_motor(motor)
+                    break
+                except Exception as e:
+                    if _ == num_retry:
+                        raise e
+                    time.sleep(0.01)
+
+    @contextmanager
+    def torque_disabled(self, motors: str | list[str] | None = None):
+        """
+        Context manager that guarantees torque is re-enabled.
+        
+        This helper is useful to temporarily disable torque when configuring motors.
+        
+        Examples:
+            >>> with bus.torque_disabled():
+            ...     # Safe operations here with torque disabled
+            ...     pass
+        """
+        self.disable_torque(motors)
+        try:
+            yield
+        finally:
+            self.enable_torque(motors)
+
+    def set_zero_position(self, motors: str | list[str] | None = None) -> None:
+        """Set current position as zero for selected motors."""
+        motors = self._get_motors_list(motors)
+        for motor in motors:
+            motor_id = self._get_motor_id(motor)
+            recv_id = self._get_motor_recv_id(motor)
+            data = [0xFF] * 7 + [CAN_CMD_SET_ZERO]
+            msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+            self.canbus.send(msg)
+            self._recv_motor_response(expected_recv_id=recv_id)
+            time.sleep(0.01)
+
+    def _refresh_motor(self, motor: NameOrID) -> Optional[can.Message]:
+        """Refresh motor status and return the response."""
+        motor_id = self._get_motor_id(motor)
+        recv_id = self._get_motor_recv_id(motor)
+        data = [motor_id & 0xFF, (motor_id >> 8) & 0xFF, CAN_CMD_REFRESH, 0, 0, 0, 0, 0]
+        msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False)
+        self.canbus.send(msg)
+        return self._recv_motor_response(expected_recv_id=recv_id)
+
+    def _recv_motor_response(self, expected_recv_id: Optional[int] = None, timeout: float = 0.001) -> Optional[can.Message]:
+        """
+        Receive a response from a motor.
+        
+        Args:
+            expected_recv_id: If provided, only return messages from this CAN ID
+            timeout: Timeout in seconds (default: 1ms for high-speed operation)
+        
+        Returns:
+            CAN message if received, None otherwise
+        """
+        try:
+            start_time = time.time()
+            messages_seen = []
+            while time.time() - start_time < timeout:
+                msg = self.canbus.recv(timeout=0.0001)  # 100us timeout for fast polling
+                if msg:
+                    messages_seen.append(f"0x{msg.arbitration_id:02X}")
+                    # If no filter specified, return any message
+                    if expected_recv_id is None:
+                        return msg
+                    # Otherwise, only return if it matches the expected recv_id
+                    if msg.arbitration_id == expected_recv_id:
+                        return msg
+                    else:
+                        logger.debug(f"Ignoring message from CAN ID 0x{msg.arbitration_id:02X}, expected 0x{expected_recv_id:02X}")
+            
+            # Only log warnings if we're in debug mode to reduce overhead
+            if logger.isEnabledFor(logging.DEBUG):
+                if messages_seen:
+                    logger.debug(f"Received {len(messages_seen)} message(s) from IDs {set(messages_seen)}, but expected 0x{expected_recv_id:02X}")
+                else:
+                    logger.debug(f"No CAN messages received (expected from 0x{expected_recv_id:02X})")
+        except Exception as e:
+            logger.debug(f"Failed to receive CAN message: {e}")
+        return None
+    
+    def _recv_all_responses(self, expected_recv_ids: list[int], timeout: float = 0.002) -> dict[int, can.Message]:
+        """
+        Efficiently receive responses from multiple motors at once.
+        Uses the OpenArms pattern: collect all available messages within timeout.
+        
+        Args:
+            expected_recv_ids: List of CAN IDs we expect responses from
+            timeout: Total timeout in seconds (default: 2ms)
+        
+        Returns:
+            Dictionary mapping recv_id to CAN message
+        """
+        responses = {}
+        expected_set = set(expected_recv_ids)
+        start_time = time.time()
+        
+        try:
+            while len(responses) < len(expected_recv_ids) and (time.time() - start_time) < timeout:
+                msg = self.canbus.recv(timeout=0.0002)  # 200us poll timeout (increased from 100us for better reliability)
+                if msg and msg.arbitration_id in expected_set:
+                    responses[msg.arbitration_id] = msg
+                    if len(responses) == len(expected_recv_ids):
+                        break  # Got all responses, exit early
+        except Exception as e:
+            logger.debug(f"Error receiving responses: {e}")
+        
+        return responses
+
+    def _mit_control(
+        self,
+        motor: NameOrID,
+        kp: float,
+        kd: float,
+        position_degrees: float,
+        velocity_deg_per_sec: float,
+        torque: float,
+    ) -> None:
+        """
+        Send MIT control command to a motor.
+
+        Args:
+            motor: Motor name or ID
+            kp: Position gain
+            kd: Velocity gain
+            position_degrees: Target position (degrees)
+            velocity_deg_per_sec: Target velocity (degrees/s)
+            torque: Target torque (N·m)
+        """
+        motor_id = self._get_motor_id(motor)
+        motor_name = self._get_motor_name(motor)
+        motor_type = self._motor_types.get(motor_name, MotorType.DM4310)
+        
+        # Convert degrees to radians for motor control
+        position_rad = np.radians(position_degrees)
+        velocity_rad_per_sec = np.radians(velocity_deg_per_sec)
+        
+        # Get motor limits
+        pmax, vmax, tmax = MOTOR_LIMIT_PARAMS[motor_type]
+        
+        # Encode parameters
+        kp_uint = self._float_to_uint(kp, 0, 500, 12)
+        kd_uint = self._float_to_uint(kd, 0, 5, 12)
+        q_uint = self._float_to_uint(position_rad, -pmax, pmax, 16)
+        dq_uint = self._float_to_uint(velocity_rad_per_sec, -vmax, vmax, 12)
+        tau_uint = self._float_to_uint(torque, -tmax, tmax, 12)
+        
+        # Pack data
+        data = [0] * 8
+        data[0] = (q_uint >> 8) & 0xFF
+        data[1] = q_uint & 0xFF
+        data[2] = dq_uint >> 4
+        data[3] = ((dq_uint & 0xF) << 4) | ((kp_uint >> 8) & 0xF)
+        data[4] = kp_uint & 0xFF
+        data[5] = kd_uint >> 4
+        data[6] = ((kd_uint & 0xF) << 4) | ((tau_uint >> 8) & 0xF)
+        data[7] = tau_uint & 0xFF
+        
+        msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+        self.canbus.send(msg)
+        recv_id = self._get_motor_recv_id(motor)
+        self._recv_motor_response(expected_recv_id=recv_id)
+    
+    def _mit_control_batch(
+        self,
+        commands: Dict[NameOrID, Tuple[float, float, float, float, float]],
+    ) -> None:
+        """
+        Send MIT control commands to multiple motors in batch (optimized).
+        Sends all commands first, then collects responses. Much faster than sequential.
+        
+        Args:
+            commands: Dict mapping motor name/ID to (kp, kd, position_deg, velocity_deg/s, torque)
+                     Example: {'joint_1': (10.0, 0.5, 45.0, 0.0, 0.0), ...}
+        """
+        if not commands:
+            return
+        
+        expected_recv_ids = []
+        
+        # Step 1: Send all MIT control commands (no waiting)
+        for motor, (kp, kd, position_degrees, velocity_deg_per_sec, torque) in commands.items():
+            motor_id = self._get_motor_id(motor)
+            motor_name = self._get_motor_name(motor)
+            motor_type = self._motor_types.get(motor_name, MotorType.DM4310)
+            
+            # Convert degrees to radians
+            position_rad = np.radians(position_degrees)
+            velocity_rad_per_sec = np.radians(velocity_deg_per_sec)
+            
+            # Get motor limits
+            pmax, vmax, tmax = MOTOR_LIMIT_PARAMS[motor_type]
+            
+            # Encode parameters
+            kp_uint = self._float_to_uint(kp, 0, 500, 12)
+            kd_uint = self._float_to_uint(kd, 0, 5, 12)
+            q_uint = self._float_to_uint(position_rad, -pmax, pmax, 16)
+            dq_uint = self._float_to_uint(velocity_rad_per_sec, -vmax, vmax, 12)
+            tau_uint = self._float_to_uint(torque, -tmax, tmax, 12)
+            
+            # Pack data
+            data = [0] * 8
+            data[0] = (q_uint >> 8) & 0xFF
+            data[1] = q_uint & 0xFF
+            data[2] = dq_uint >> 4
+            data[3] = ((dq_uint & 0xF) << 4) | ((kp_uint >> 8) & 0xF)
+            data[4] = kp_uint & 0xFF
+            data[5] = kd_uint >> 4
+            data[6] = ((kd_uint & 0xF) << 4) | ((tau_uint >> 8) & 0xF)
+            data[7] = tau_uint & 0xFF
+            
+            # Send command
+            msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+            self.canbus.send(msg)
+            
+            # Track expected response
+            recv_id = self._get_motor_recv_id(motor)
+            expected_recv_ids.append(recv_id)
+        
+        # Step 2: Collect all responses at once
+        self._recv_all_responses(expected_recv_ids, timeout=0.002)
+
+    def _float_to_uint(self, x: float, x_min: float, x_max: float, bits: int) -> int:
+        """Convert float to unsigned integer for CAN transmission."""
+        x = max(x_min, min(x_max, x))  # Clamp to range
+        span = x_max - x_min
+        data_norm = (x - x_min) / span
+        return int(data_norm * ((1 << bits) - 1))
+
+    def _uint_to_float(self, x: int, x_min: float, x_max: float, bits: int) -> float:
+        """Convert unsigned integer from CAN to float."""
+        span = x_max - x_min
+        data_norm = float(x) / ((1 << bits) - 1)
+        return data_norm * span + x_min
+
+    def _decode_motor_state(self, data: bytes, motor_type: MotorType) -> Tuple[float, float, float, int, int]:
+        """
+        Decode motor state from CAN data.
+
+        Returns:
+            Tuple of (position_degrees, velocity_deg_per_sec, torque, temp_mos, temp_rotor)
+        """
+        if len(data) < 8:
+            raise ValueError("Invalid motor state data")
+        
+        # Extract encoded values
+        q_uint = (data[1] << 8) | data[2]
+        dq_uint = (data[3] << 4) | (data[4] >> 4)
+        tau_uint = ((data[4] & 0x0F) << 8) | data[5]
+        t_mos = data[6]
+        t_rotor = data[7]
+        
+        # Get motor limits
+        pmax, vmax, tmax = MOTOR_LIMIT_PARAMS[motor_type]
+        
+        # Decode to physical values (radians)
+        position_rad = self._uint_to_float(q_uint, -pmax, pmax, 16)
+        velocity_rad_per_sec = self._uint_to_float(dq_uint, -vmax, vmax, 12)
+        torque = self._uint_to_float(tau_uint, -tmax, tmax, 12)
+        
+        # Convert to degrees
+        position_degrees = np.degrees(position_rad)
+        velocity_deg_per_sec = np.degrees(velocity_rad_per_sec)
+        
+        return position_degrees, velocity_deg_per_sec, torque, t_mos, t_rotor
+
+    def read(
+        self,
+        data_name: str,
+        motor: str,
+        *,
+        normalize: bool = True,
+        num_retry: int = 0,
+    ) -> Value:
+        """Read a value from a single motor. Positions are always in degrees."""
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+        
+        # Refresh motor to get latest state
+        msg = self._refresh_motor(motor)
+        if msg is None:
+            motor_id = self._get_motor_id(motor)
+            recv_id = self._get_motor_recv_id(motor)
+            raise ConnectionError(
+                f"No response from motor '{motor}' (send ID: 0x{motor_id:02X}, recv ID: 0x{recv_id:02X}). "
+                f"Check that: 1) Motor is powered (24V), 2) CAN wiring is correct, "
+                f"3) Motor IDs are configured correctly using Damiao Debugging Tools"
+            )
+        
+        motor_type = self._motor_types.get(motor, MotorType.DM4310)
+        position_degrees, velocity_deg_per_sec, torque, t_mos, t_rotor = self._decode_motor_state(msg.data, motor_type)
+        
+        # Return requested data (already in degrees for position/velocity)
+        if data_name == "Present_Position":
+            value = position_degrees
+        elif data_name == "Present_Velocity":
+            value = velocity_deg_per_sec
+        elif data_name == "Present_Torque":
+            value = torque
+        elif data_name == "Temperature_MOS":
+            value = t_mos
+        elif data_name == "Temperature_Rotor":
+            value = t_rotor
+        else:
+            raise ValueError(f"Unknown data_name: {data_name}")
+        
+        # For Damiao, positions are always in degrees, no normalization needed
+        # We keep the normalize parameter for compatibility but don't use it
+        return value
+
+    def write(
+        self,
+        data_name: str,
+        motor: str,
+        value: Value,
+        *,
+        normalize: bool = True,
+        num_retry: int = 0,
+    ) -> None:
+        """Write a value to a single motor. Positions are always in degrees."""
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+        
+        # Value is expected to be in degrees for positions
+        if data_name == "Goal_Position":
+            # Use MIT control with position in degrees
+            self._mit_control(motor, 10.0, 0.5, value, 0, 0)
+        else:
+            raise ValueError(f"Writing {data_name} not supported in MIT mode")
+
+    def sync_read(
+        self,
+        data_name: str,
+        motors: str | list[str] | None = None,
+        *,
+        normalize: bool = True,
+        num_retry: int = 0,
+    ) -> Dict[str, Value]:
+        """
+        Read the same value from multiple motors simultaneously.
+        Uses batched operations: sends all refresh commands, then collects all responses.
+        This is MUCH faster than sequential reads (OpenArms pattern).
+        """
+        motors = self._get_motors_list(motors)
+        result = {}
+
+        # Step 1: Send refresh commands to ALL motors first (no waiting)
+        for motor in motors:
+            motor_id = self._get_motor_id(motor)
+            data = [motor_id & 0xFF, (motor_id >> 8) & 0xFF, CAN_CMD_REFRESH, 0, 0, 0, 0, 0]
+            msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False)
+            self.canbus.send(msg)
+        
+        # Step 2: Collect all responses at once (batch receive)
+        expected_recv_ids = [self._get_motor_recv_id(motor) for motor in motors]
+        responses = self._recv_all_responses(expected_recv_ids, timeout=0.01)  # 10ms total timeout
+        
+        # Step 3: Parse responses
+        for motor in motors:
+            try:
+                recv_id = self._get_motor_recv_id(motor)
+                msg = responses.get(recv_id)
+                
+                if msg is None:
+                    logger.warning(f"No response from motor '{motor}' (recv ID: 0x{recv_id:02X})")
+                    result[motor] = 0.0
+                    continue
+                
+                motor_type = self._motor_types.get(motor, MotorType.DM4310)
+                position_degrees, velocity_deg_per_sec, torque, t_mos, t_rotor = self._decode_motor_state(msg.data, motor_type)
+                
+                # Return requested data
+                if data_name == "Present_Position":
+                    value = position_degrees
+                elif data_name == "Present_Velocity":
+                    value = velocity_deg_per_sec
+                elif data_name == "Present_Torque":
+                    value = torque
+                elif data_name == "Temperature_MOS":
+                    value = t_mos
+                elif data_name == "Temperature_Rotor":
+                    value = t_rotor
+                else:
+                    raise ValueError(f"Unknown data_name: {data_name}")
+                
+                result[motor] = value
+                
+            except Exception as e:
+                logger.warning(f"Failed to read {data_name} from {motor}: {e}")
+                result[motor] = 0.0
+        
+        return result
+    
+    def sync_read_all_states(
+        self,
+        motors: str | list[str] | None = None,
+        *,
+        num_retry: int = 0,
+    ) -> Dict[str, Dict[str, Value]]:
+        """
+        Read ALL motor states (position, velocity, torque) from multiple motors in ONE refresh cycle.
+        This is 3x faster than calling sync_read() three times separately.
+        
+        Returns:
+            Dictionary mapping motor names to state dicts with keys: 'position', 'velocity', 'torque'
+            Example: {'joint_1': {'position': 45.2, 'velocity': 1.3, 'torque': 0.5}, ...}
+        """
+        motors = self._get_motors_list(motors)
+        result = {}
+
+        # Step 1: Send refresh commands to ALL motors first (with small delays to reduce bus congestion)
+        for motor in motors:
+            motor_id = self._get_motor_id(motor)
+            data = [motor_id & 0xFF, (motor_id >> 8) & 0xFF, CAN_CMD_REFRESH, 0, 0, 0, 0, 0]
+            msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False)
+            self.canbus.send(msg)
+            time.sleep(0.0001)  # 100us delay between commands to reduce bus congestion
+        
+        # Step 2: Collect all responses at once (batch receive)
+        expected_recv_ids = [self._get_motor_recv_id(motor) for motor in motors]
+        responses = self._recv_all_responses(expected_recv_ids, timeout=0.015)  # 15ms timeout (increased for reliability)
+        
+        # Step 3: Parse responses and extract ALL state values
+        for motor in motors:
+            try:
+                recv_id = self._get_motor_recv_id(motor)
+                msg = responses.get(recv_id)
+                
+                if msg is None:
+                    logger.warning(f"No response from motor '{motor}' (recv ID: 0x{recv_id:02X})")
+                    result[motor] = {"position": 0.0, "velocity": 0.0, "torque": 0.0}
+                    continue
+                
+                motor_type = self._motor_types.get(motor, MotorType.DM4310)
+                position_degrees, velocity_deg_per_sec, torque, t_mos, t_rotor = self._decode_motor_state(msg.data, motor_type)
+                
+                # Return all state values in one dict
+                result[motor] = {
+                    "position": position_degrees,
+                    "velocity": velocity_deg_per_sec,
+                    "torque": torque,
+                    "temp_mos": t_mos,
+                    "temp_rotor": t_rotor,
+                }
+                
+            except Exception as e:
+                logger.warning(f"Failed to read state from {motor}: {e}")
+                result[motor] = {"position": 0.0, "velocity": 0.0, "torque": 0.0}
+        
+        return result
+
+    def sync_write(
+        self,
+        data_name: str,
+        values: Dict[str, Value],
+        *,
+        normalize: bool = True,
+        num_retry: int = 0,
+    ) -> None:
+        """
+        Write different values to multiple motors simultaneously. Positions are always in degrees.
+        Uses batched operations: sends all commands first, then collects responses (OpenArms pattern).
+        """
+        if data_name == "Goal_Position":
+            # Step 1: Send all MIT control commands first (no waiting)
+            for motor, value_degrees in values.items():
+                motor_id = self._get_motor_id(motor)
+                motor_name = self._get_motor_name(motor)
+                motor_type = self._motor_types.get(motor_name, MotorType.DM4310)
+                
+                # Convert degrees to radians
+                position_rad = np.radians(value_degrees)
+                
+                # Default gains for position control
+                kp, kd = 10.0, 0.5
+                
+                # Get motor limits and encode parameters
+                pmax, vmax, tmax = MOTOR_LIMIT_PARAMS[motor_type]
+                kp_uint = self._float_to_uint(kp, 0, 500, 12)
+                kd_uint = self._float_to_uint(kd, 0, 5, 12)
+                q_uint = self._float_to_uint(position_rad, -pmax, pmax, 16)
+                dq_uint = self._float_to_uint(0, -vmax, vmax, 12)
+                tau_uint = self._float_to_uint(0, -tmax, tmax, 12)
+                
+                # Pack data
+                data = [0] * 8
+                data[0] = (q_uint >> 8) & 0xFF
+                data[1] = q_uint & 0xFF
+                data[2] = dq_uint >> 4
+                data[3] = ((dq_uint & 0xF) << 4) | ((kp_uint >> 8) & 0xF)
+                data[4] = kp_uint & 0xFF
+                data[5] = kd_uint >> 4
+                data[6] = ((kd_uint & 0xF) << 4) | ((tau_uint >> 8) & 0xF)
+                data[7] = tau_uint & 0xFF
+                
+                msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+                self.canbus.send(msg)
+                time.sleep(0.0001)  # 100us delay between commands to reduce bus congestion
+            
+            # Step 2: Collect all responses at once
+            expected_recv_ids = [self._get_motor_recv_id(motor) for motor in values.keys()]
+            self._recv_all_responses(expected_recv_ids, timeout=0.015)  # 15ms timeout (increased for reliability)
+        else:
+            # Fall back to individual writes for other data types
+            for motor, value in values.items():
+                self.write(data_name, motor, value, normalize=normalize, num_retry=num_retry)
+
+    def read_calibration(self) -> dict[str, MotorCalibration]:
+        """Read calibration data from motors."""
+        # Damiao motors don't store calibration internally
+        # Return existing calibration or empty dict
+        return self.calibration if self.calibration else {}
+
+    def write_calibration(self, calibration_dict: dict[str, MotorCalibration], cache: bool = True) -> None:
+        """Write calibration data to motors."""
+        # Damiao motors don't store calibration internally
+        # Just cache it in memory
+        if cache:
+            self.calibration = calibration_dict
+
+    def record_ranges_of_motion(
+        self, motors: NameOrID | list[NameOrID] | None = None, display_values: bool = True
+    ) -> tuple[dict[NameOrID, Value], dict[NameOrID, Value]]:
+        """
+        Interactively record the min/max values of each motor in degrees.
+
+        Move the joints by hand (with torque disabled) while the method streams live positions.
+        Press Enter to finish.
+        """
+        if motors is None:
+            motors = list(self.motors.keys())
+        elif isinstance(motors, (str, int)):
+            motors = [motors]
+
+        # Disable torque for manual movement
+        self.disable_torque(motors)
+        time.sleep(0.1)
+
+        # Get initial positions (already in degrees)
+        start_positions = self.sync_read("Present_Position", motors, normalize=False)
+        mins = start_positions.copy()
+        maxes = start_positions.copy()
+
+        print("\nMove joints through their full range of motion. Press ENTER when done.")
+        user_pressed_enter = False
+        
+        while not user_pressed_enter:
+            positions = self.sync_read("Present_Position", motors, normalize=False)
+            
+            for motor in motors:
+                if motor in positions:
+                    mins[motor] = min(positions[motor], mins.get(motor, positions[motor]))
+                    maxes[motor] = max(positions[motor], maxes.get(motor, positions[motor]))
+
+            if display_values:
+                print("\n" + "=" * 50)
+                print(f"{'MOTOR':<20} | {'MIN (deg)':>12} | {'POS (deg)':>12} | {'MAX (deg)':>12}")
+                print("-" * 50)
+                for motor in motors:
+                    if motor in positions:
+                        print(f"{motor:<20} | {mins[motor]:>12.1f} | {positions[motor]:>12.1f} | {maxes[motor]:>12.1f}")
+
+            if enter_pressed():
+                user_pressed_enter = True
+
+            if display_values and not user_pressed_enter:
+                # Move cursor up to overwrite the previous output
+                move_cursor_up(len(motors) + 4)
+
+            time.sleep(0.05)
+
+        # Re-enable torque
+        self.enable_torque(motors)
+        
+        # Validate ranges
+        for motor in motors:
+            if motor in mins and motor in maxes:
+                if abs(maxes[motor] - mins[motor]) < 5.0:  # At least 5 degrees of range
+                    raise ValueError(f"Motor {motor} has insufficient range of motion (< 5 degrees)")
+
+        return mins, maxes
+
+    def _get_motors_list(self, motors: str | list[str] | None) -> list[str]:
+        """Convert motor specification to list of motor names."""
+        if motors is None:
+            return list(self.motors.keys())
+        elif isinstance(motors, str):
+            return [motors]
+        elif isinstance(motors, list):
+            return motors
+        else:
+            raise TypeError(f"Invalid motors type: {type(motors)}")
+
+    def _get_motor_id(self, motor: NameOrID) -> int:
+        """Get CAN ID for a motor."""
+        if isinstance(motor, str):
+            if motor in self.motors:
+                return self.motors[motor].id
+            else:
+                raise ValueError(f"Unknown motor: {motor}")
+        else:
+            return motor
+
+    def _get_motor_name(self, motor: NameOrID) -> str:
+        """Get motor name from name or ID."""
+        if isinstance(motor, str):
+            return motor
+        else:
+            for name, m in self.motors.items():
+                if m.id == motor:
+                    return name
+            raise ValueError(f"Unknown motor ID: {motor}")
+    
+    def _get_motor_recv_id(self, motor: NameOrID) -> Optional[int]:
+        """Get motor recv_id from name or ID."""
+        motor_name = self._get_motor_name(motor)
+        motor_obj = self.motors.get(motor_name)
+        if motor_obj and hasattr(motor_obj, "recv_id"):
+            return motor_obj.recv_id
+        return None
+
+    @cached_property
+    def is_calibrated(self) -> bool:
+        """Check if motors are calibrated."""
+        return bool(self.calibration)

src/lerobot/motors/damiao/tables.py

@@ -0,0 +1,209 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Configuration tables for Damiao motors."""
+
+from enum import IntEnum
+from typing import Dict, List, Tuple
+
+# Motor type definitions
+class MotorType(IntEnum):
+    DM3507 = 0
+    DM4310 = 1
+    DM4310_48V = 2
+    DM4340 = 3
+    DM4340_48V = 4
+    DM6006 = 5
+    DM8006 = 6
+    DM8009 = 7
+    DM10010L = 8
+    DM10010 = 9
+    DMH3510 = 10
+    DMH6215 = 11
+    DMG6220 = 12
+
+# Control modes
+class ControlMode(IntEnum):
+    MIT = 1
+    POS_VEL = 2
+    VEL = 3
+    TORQUE_POS = 4
+
+# Motor variable IDs (RID)
+class MotorVariable(IntEnum):
+    UV_VALUE = 0
+    KT_VALUE = 1
+    OT_VALUE = 2
+    OC_VALUE = 3
+    ACC = 4
+    DEC = 5
+    MAX_SPD = 6
+    MST_ID = 7
+    ESC_ID = 8
+    TIMEOUT = 9
+    CTRL_MODE = 10
+    DAMP = 11
+    INERTIA = 12
+    HW_VER = 13
+    SW_VER = 14
+    SN = 15
+    NPP = 16
+    RS = 17
+    LS = 18
+    FLUX = 19
+    GR = 20
+    PMAX = 21
+    VMAX = 22
+    TMAX = 23
+    I_BW = 24
+    KP_ASR = 25
+    KI_ASR = 26
+    KP_APR = 27
+    KI_APR = 28
+    OV_VALUE = 29
+    GREF = 30
+    DETA = 31
+    V_BW = 32
+    IQ_C1 = 33
+    VL_C1 = 34
+    CAN_BR = 35
+    SUB_VER = 36
+    U_OFF = 50
+    V_OFF = 51
+    K1 = 52
+    K2 = 53
+    M_OFF = 54
+    DIR = 55
+    P_M = 80
+    XOUT = 81
+
+# Motor limit parameters [PMAX, VMAX, TMAX] 
+# PMAX: Maximum position (rad)
+# VMAX: Maximum velocity (rad/s)
+# TMAX: Maximum torque (N·m)
+MOTOR_LIMIT_PARAMS = {
+    MotorType.DM3507: (12.5, 30, 10),
+    MotorType.DM4310: (12.5, 30, 10),
+    MotorType.DM4310_48V: (12.5, 50, 10),
+    MotorType.DM4340: (12.5, 8, 28),
+    MotorType.DM4340_48V: (12.5, 10, 28),
+    MotorType.DM6006: (12.5, 45, 20),
+    MotorType.DM8006: (12.5, 45, 40),
+    MotorType.DM8009: (12.5, 45, 54),
+    MotorType.DM10010L: (12.5, 25, 200),
+    MotorType.DM10010: (12.5, 20, 200),
+    MotorType.DMH3510: (12.5, 280, 1),
+    MotorType.DMH6215: (12.5, 45, 10),
+    MotorType.DMG6220: (12.5, 45, 10),
+}
+
+# Motor model names
+MODEL_NAMES = {
+    MotorType.DM3507: "dm3507",
+    MotorType.DM4310: "dm4310",
+    MotorType.DM4310_48V: "dm4310_48v",
+    MotorType.DM4340: "dm4340",
+    MotorType.DM4340_48V: "dm4340_48v",
+    MotorType.DM6006: "dm6006",
+    MotorType.DM8006: "dm8006",
+    MotorType.DM8009: "dm8009",
+    MotorType.DM10010L: "dm10010l",
+    MotorType.DM10010: "dm10010",
+    MotorType.DMH3510: "dmh3510",
+    MotorType.DMH6215: "dmh6215",
+    MotorType.DMG6220: "dmg6220",
+}
+
+# Motor resolution table (encoder counts per revolution)
+MODEL_RESOLUTION = {
+    "dm3507": 65536,
+    "dm4310": 65536,
+    "dm4310_48v": 65536,
+    "dm4340": 65536,
+    "dm4340_48v": 65536,
+    "dm6006": 65536,
+    "dm8006": 65536,
+    "dm8009": 65536,
+    "dm10010l": 65536,
+    "dm10010": 65536,
+    "dmh3510": 65536,
+    "dmh6215": 65536,
+    "dmg6220": 65536,
+}
+
+# CAN baudrates supported by Damiao motors
+AVAILABLE_BAUDRATES = [
+    125000,   # 0: 125 kbps
+    200000,   # 1: 200 kbps
+    250000,   # 2: 250 kbps
+    500000,   # 3: 500 kbps
+    1000000,  # 4: 1 mbps (default for OpenArms)
+    2000000,  # 5: 2 mbps
+    2500000,  # 6: 2.5 mbps
+    3200000,  # 7: 3.2 mbps
+    4000000,  # 8: 4 mbps
+    5000000,  # 9: 5 mbps
+]
+DEFAULT_BAUDRATE = 1000000  # 1 Mbps is standard for OpenArms
+
+# Default timeout in milliseconds
+DEFAULT_TIMEOUT_MS = 1000
+
+# Data that should be normalized
+NORMALIZED_DATA = ["Present_Position", "Goal_Position"]
+
+# OpenArms specific configurations
+# Based on: https://docs.openarm.dev/software/setup/configure-test
+# OpenArms has 7 DOF per arm (14 total for dual arm)
+OPENARMS_ARM_MOTOR_IDS = {
+    "joint_1": {"send": 0x01, "recv": 0x11},  # J1 - Shoulder pan
+    "joint_2": {"send": 0x02, "recv": 0x12},  # J2 - Shoulder lift
+    "joint_3": {"send": 0x03, "recv": 0x13},  # J3 - Elbow flex
+    "joint_4": {"send": 0x04, "recv": 0x14},  # J4 - Wrist flex
+    "joint_5": {"send": 0x05, "recv": 0x15},  # J5 - Wrist roll
+    "joint_6": {"send": 0x06, "recv": 0x16},  # J6 - Wrist pitch
+    "joint_7": {"send": 0x07, "recv": 0x17},  # J7 - Wrist rotation
+}
+
+OPENARMS_GRIPPER_MOTOR_IDS = {
+    "gripper": {"send": 0x08, "recv": 0x18},  # J8 - Gripper
+}
+
+# Default motor types for OpenArms
+OPENARMS_DEFAULT_MOTOR_TYPES = {
+    "joint_1": MotorType.DM8009,   # Shoulder pan - high torque
+    "joint_2": MotorType.DM8009,   # Shoulder lift - high torque
+    "joint_3": MotorType.DM4340,   # Shoulder rotation
+    "joint_4": MotorType.DM4340,   # Elbow flex
+    "joint_5": MotorType.DM4310,   # Wrist roll
+    "joint_6": MotorType.DM4310,   # Wrist pitch
+    "joint_7": MotorType.DM4310,   # Wrist rotation
+    "gripper": MotorType.DM4310,   # Gripper
+}
+
+# MIT control parameter ranges
+MIT_KP_RANGE = (0.0, 500.0)
+MIT_KD_RANGE = (0.0, 5.0)
+
+# CAN frame command IDs
+CAN_CMD_ENABLE = 0xFC
+CAN_CMD_DISABLE = 0xFD
+CAN_CMD_SET_ZERO = 0xFE
+CAN_CMD_REFRESH = 0xCC
+CAN_CMD_QUERY_PARAM = 0x33
+CAN_CMD_WRITE_PARAM = 0x55
+CAN_CMD_SAVE_PARAM = 0xAA
+
+# CAN ID for parameter operations
+CAN_PARAM_ID = 0x7FF

src/lerobot/motors/dynamixel/dynamixel.py

@@ -24,7 +24,7 @@ from enum import Enum
 
 from lerobot.motors.encoding_utils import decode_twos_complement, encode_twos_complement
 
-from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address
+from ..motors_bus import Motor, MotorCalibration, NameOrID, SerialMotorsBus, Value, get_address
 from .tables import (
     AVAILABLE_BAUDRATES,
     MODEL_BAUDRATE_TABLE,
@@ -100,7 +100,7 @@ def _split_into_byte_chunks(value: int, length: int) -> list[int]:
     return data
 
 
-class DynamixelMotorsBus(MotorsBus):
+class DynamixelMotorsBus(SerialMotorsBus):
     """
     The Dynamixel implementation for a MotorsBus. It relies on the python dynamixel sdk to communicate with
     the motors. For more info, see the Dynamixel SDK Documentation:

src/lerobot/motors/feetech/feetech.py

@@ -19,7 +19,7 @@ from pprint import pformat
 
 from lerobot.motors.encoding_utils import decode_sign_magnitude, encode_sign_magnitude
 
-from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address
+from ..motors_bus import Motor, MotorCalibration, NameOrID, SerialMotorsBus, Value, get_address
 from .tables import (
     FIRMWARE_MAJOR_VERSION,
     FIRMWARE_MINOR_VERSION,
@@ -96,7 +96,7 @@ def patch_setPacketTimeout(self, packet_length):  # noqa: N802
     self.packet_timeout = (self.tx_time_per_byte * packet_length) + (self.tx_time_per_byte * 3.0) + 50
 
 
-class FeetechMotorsBus(MotorsBus):
+class FeetechMotorsBus(SerialMotorsBus):
     """
     The FeetechMotorsBus class allows to efficiently read and write to the attached motors. It relies on the
     python feetech sdk to communicate with the motors, which is itself based on the dynamixel sdk.
@@ -165,7 +165,7 @@ class FeetechMotorsBus(MotorsBus):
 
     def _handshake(self) -> None:
         self._assert_motors_exist()
-        self._assert_same_firmware()
+        #self._assert_same_firmware()
 
     def _find_single_motor(self, motor: str, initial_baudrate: int | None = None) -> tuple[int, int]:
         if self.protocol_version == 0:

src/lerobot/motors/motors_bus.py

@@ -19,6 +19,8 @@
 # TODO(aliberts): Add block noqa when feature below is available
 # https://github.com/astral-sh/ruff/issues/3711
 
+from __future__ import annotations
+
 import abc
 import logging
 from contextlib import contextmanager
@@ -41,6 +43,92 @@ Value: TypeAlias = int | float
 logger = logging.getLogger(__name__)
 
 
+class MotorsBusBase(abc.ABC):
+    """
+    Base class for all motor bus implementations.
+    
+    This is a minimal interface that all motor buses must implement, regardless of their
+    communication protocol (serial, CAN, etc.).
+    """
+
+    def __init__(
+        self,
+        port: str,
+        motors: dict[str, Motor],
+        calibration: dict[str, MotorCalibration] | None = None,
+    ):
+        self.port = port
+        self.motors = motors
+        self.calibration = calibration if calibration else {}
+
+    @abc.abstractmethod
+    def connect(self, handshake: bool = True) -> None:
+        """Establish connection to the motors."""
+        pass
+
+    @abc.abstractmethod
+    def disconnect(self, disable_torque: bool = True) -> None:
+        """Disconnect from the motors."""
+        pass
+
+    @property
+    @abc.abstractmethod
+    def is_connected(self) -> bool:
+        """Check if connected to the motors."""
+        pass
+
+    @abc.abstractmethod
+    def read(self, data_name: str, motor: str, *, normalize: bool = True, num_retry: int = 0) -> Value:
+        """Read a value from a single motor."""
+        pass
+
+    @abc.abstractmethod
+    def write(
+        self, data_name: str, motor: str, value: Value, *, normalize: bool = True, num_retry: int = 0
+    ) -> None:
+        """Write a value to a single motor."""
+        pass
+
+    @abc.abstractmethod
+    def sync_read(
+        self, data_name: str, motors: str | list[str] | None = None, *, normalize: bool = True
+    ) -> dict[str, Value]:
+        """Read a value from multiple motors."""
+        pass
+
+    @abc.abstractmethod
+    def sync_write(
+        self,
+        data_name: str,
+        values: Value | dict[str, Value],
+        motors: str | list[str] | None = None,
+        *,
+        normalize: bool = True,
+    ) -> None:
+        """Write values to multiple motors."""
+        pass
+
+    @abc.abstractmethod
+    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+        """Enable torque on selected motors."""
+        pass
+
+    @abc.abstractmethod
+    def disable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
+        """Disable torque on selected motors."""
+        pass
+
+    @abc.abstractmethod
+    def read_calibration(self) -> dict[str, MotorCalibration]:
+        """Read calibration parameters from the motors."""
+        pass
+
+    @abc.abstractmethod
+    def write_calibration(self, calibration_dict: dict[str, MotorCalibration], cache: bool = True) -> None:
+        """Write calibration parameters to the motors."""
+        pass
+
+
 def get_ctrl_table(model_ctrl_table: dict[str, dict], model: str) -> dict[str, tuple[int, int]]:
     ctrl_table = model_ctrl_table.get(model)
     if ctrl_table is None:
@@ -203,15 +291,15 @@ class GroupSyncWrite(Protocol):
     def txPacket(self): ...
 
 
-class MotorsBus(abc.ABC):
+class SerialMotorsBus(MotorsBusBase):
     """
-    A MotorsBus allows to efficiently read and write to the attached motors.
+    A SerialMotorsBus allows to efficiently read and write to motors connected via serial communication.
     It represents several motors daisy-chained together and connected through a serial port.
-    There are currently two implementations of this abstract class:
+    There are currently two implementations of this class:
         - DynamixelMotorsBus
         - FeetechMotorsBus
 
-    Note: This class may evolve in the future should we add support for other types of bus.
+    This class is specifically for serial-based motor protocols (Dynamixel, Feetech, etc.).
 
     A MotorsBus subclass instance requires a port (e.g. `FeetechMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
     To find the port, you can run our utility script:
@@ -1212,3 +1300,7 @@ class MotorsBus(abc.ABC):
         for id_, value in ids_values.items():
             data = self._serialize_data(value, length)
             self.sync_writer.addParam(id_, data)
+
+
+# Backward compatibility alias
+MotorsBus = SerialMotorsBus

src/lerobot/optim/optimizers.py

@@ -104,6 +104,107 @@ 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

@@ -21,6 +21,7 @@ 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",
@@ -31,4 +32,5 @@ __all__ = [
     "TDMPCConfig",
     "VQBeTConfig",
     "GrootConfig",
+    "XVLAConfig",
 ]

src/lerobot/policies/factory.py

@@ -16,6 +16,7 @@
 
 from __future__ import annotations
 
+import importlib
 import logging
 from typing import Any, TypedDict
 
@@ -40,6 +41,7 @@ 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,
@@ -107,8 +109,15 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from lerobot.policies.groot.modeling_groot import GrootPolicy
 
         return GrootPolicy
+    elif name == "xvla":
+        from lerobot.policies.xvla.modeling_xvla import XVLAPolicy
+
+        return XVLAPolicy
     else:
-        raise NotImplementedError(f"Policy with name {name} is not implemented.")
+        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
 
 
 def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
@@ -150,8 +159,14 @@ 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:
-        raise ValueError(f"Policy type '{policy_type}' is not available.")
+        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
 
 
 class ProcessorConfigKwargs(TypedDict, total=False):
@@ -329,9 +344,24 @@ 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:
-        raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.")
+        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
 
     return processors
 
@@ -400,8 +430,7 @@ def make_policy(
             raise ValueError("env_cfg cannot be None when ds_meta is not provided")
         features = env_to_policy_features(env_cfg)
 
-    if not cfg.output_features:
-        cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
+    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
@@ -425,3 +454,65 @@ def make_policy(
         # TODO: (jadechoghari) - add a check_state(cfg, features) and check_action(cfg, features)
 
     return policy
+
+
+def _get_policy_cls_from_policy_name(name: str) -> type[PreTrainedConfig]:
+    """Get policy class from its registered name using dynamic imports.
+
+    This is used as a helper function to import policies from 3rd party lerobot plugins.
+
+    Args:
+        name: The name of the policy.
+    Returns:
+        The policy class corresponding to the given name.
+    """
+    if name not in PreTrainedConfig.get_known_choices():
+        raise ValueError(
+            f"Unknown policy name '{name}'. Available policies: {PreTrainedConfig.get_known_choices()}"
+        )
+
+    config_cls = PreTrainedConfig.get_choice_class(name)
+    config_cls_name = config_cls.__name__
+
+    model_name = config_cls_name.removesuffix("Config")  # e.g., DiffusionConfig -> Diffusion
+    if model_name == config_cls_name:
+        raise ValueError(
+            f"The config class name '{config_cls_name}' does not follow the expected naming convention."
+            f"Make sure it ends with 'Config'!"
+        )
+    cls_name = model_name + "Policy"  # e.g., DiffusionConfig -> DiffusionPolicy
+    module_path = config_cls.__module__.replace(
+        "configuration_", "modeling_"
+    )  # e.g., configuration_diffusion -> modeling_diffusion
+
+    module = importlib.import_module(module_path)
+    policy_cls = getattr(module, cls_name)
+    return policy_cls
+
+
+def _make_processors_from_policy_config(
+    config: PreTrainedConfig,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+) -> tuple[Any, Any]:
+    """Create pre- and post-processors from a policy configuration using dynamic imports.
+
+    This is used as a helper function to import processor factories from 3rd party lerobot plugins.
+
+    Args:
+        config: The policy configuration object.
+        dataset_stats: Dataset statistics for normalization.
+    Returns:
+        A tuple containing the input (pre-processor) and output (post-processor) pipelines.
+    """
+
+    policy_type = config.type
+    function_name = f"make_{policy_type}_pre_post_processors"
+    module_path = config.__class__.__module__.replace(
+        "configuration_", "processor_"
+    )  # e.g., configuration_diffusion -> processor_diffusion
+    logging.debug(
+        f"Instantiating pre/post processors using function '{function_name}' from module '{module_path}'"
+    )
+    module = importlib.import_module(module_path)
+    function = getattr(module, function_name)
+    return function(config, dataset_stats=dataset_stats)

src/lerobot/policies/pi0/configuration_pi0.py

@@ -23,6 +23,8 @@ from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
 from lerobot.policies.rtc.configuration_rtc import RTCConfig
 from lerobot.utils.constants import OBS_IMAGES
 
+DEFAULT_IMAGE_SIZE = 224
+
 
 @PreTrainedConfig.register_subclass("pi0")
 @dataclass
@@ -51,7 +53,10 @@ class PI0Config(PreTrainedConfig):
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 
-    image_resolution: tuple[int, int] = (224, 224)  # see openpi `preprocessing_pytorch.py`
+    image_resolution: tuple[int, int] = (
+        DEFAULT_IMAGE_SIZE,
+        DEFAULT_IMAGE_SIZE,
+    )  # see openpi `preprocessing_pytorch.py`
 
     # Add empty images. Used to add empty cameras when no image features are present.
     empty_cameras: int = 0

src/lerobot/policies/pi0/modeling_pi0.py

@@ -41,7 +41,7 @@ else:
     PaliGemmaForConditionalGeneration = None
 
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.policies.pi0.configuration_pi0 import PI0Config
+from lerobot.policies.pi0.configuration_pi0 import DEFAULT_IMAGE_SIZE, PI0Config
 from lerobot.policies.pretrained import PreTrainedPolicy, T
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor
 from lerobot.utils.constants import (
@@ -337,6 +337,7 @@ class PaliGemmaWithExpertModel(
         action_expert_config,
         use_adarms=None,
         precision: Literal["bfloat16", "float32"] = "bfloat16",
+        image_size: int = DEFAULT_IMAGE_SIZE,
     ):
         if use_adarms is None:
             use_adarms = [False, False]
@@ -356,6 +357,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.text_config.vocab_size = 257152
         vlm_config_hf.text_config.use_adarms = use_adarms[0]
         vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
+        vlm_config_hf.vision_config.image_size = image_size
         vlm_config_hf.vision_config.intermediate_size = 4304
         vlm_config_hf.vision_config.projection_dim = 2048
         vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
@@ -519,11 +521,17 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         paligemma_config = get_gemma_config(config.paligemma_variant)
         action_expert_config = get_gemma_config(config.action_expert_variant)
 
+        if config.image_resolution[0] != config.image_resolution[1]:
+            raise ValueError(
+                f"PaliGemma expects square image resolution, invalid resolution: {config.image_resolution}"
+            )
+
         self.paligemma_with_expert = PaliGemmaWithExpertModel(
             paligemma_config,
             action_expert_config,
             use_adarms=[False, False],
             precision=config.dtype,
+            image_size=config.image_resolution[0],
         )
 
         self.action_in_proj = nn.Linear(config.max_action_dim, action_expert_config.width)
@@ -812,16 +820,13 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         )
 
         dt = -1.0 / num_steps
-        dt = torch.tensor(dt, dtype=torch.float32, device=device)
 
         x_t = noise
-        time = torch.tensor(1.0, dtype=torch.float32, device=device)
-        while time >= -dt / 2:
-            expanded_time = time.expand(bsize)
+        for step in range(num_steps):
+            time = 1.0 + step * dt
+            time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)
 
-            # Define a closure function to properly capture expanded_time
-            # This avoids the lambda expression (E731) and loop variable binding (B023) issues
-            def denoise_step_partial_call(input_x_t, current_timestep=expanded_time):
+            def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
                 return self.denoise_step(
                     state=state,
                     prefix_pad_masks=prefix_pad_masks,
@@ -846,15 +851,11 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             else:
                 v_t = denoise_step_partial_call(x_t)
 
-            # Euler step
-            x_t += dt * v_t
+            x_t = x_t + dt * v_t
 
-            # Record x_t and v_t after Euler step
             if self.rtc_processor is not None and self.rtc_processor.is_debug_enabled():
                 self.rtc_processor.track(time=time, x_t=x_t, v_t=v_t)
 
-            time += dt
-
         return x_t
 
     def denoise_step(

src/lerobot/policies/pi05/configuration_pi05.py

@@ -22,6 +22,8 @@ from lerobot.optim.optimizers import AdamWConfig
 from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
 from lerobot.policies.rtc.configuration_rtc import RTCConfig
 
+DEFAULT_IMAGE_SIZE = 224
+
 
 @PreTrainedConfig.register_subclass("pi05")
 @dataclass
@@ -50,7 +52,10 @@ class PI05Config(PreTrainedConfig):
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 
-    image_resolution: tuple[int, int] = (224, 224)  # see openpi `preprocessing_pytorch.py`
+    image_resolution: tuple[int, int] = (
+        DEFAULT_IMAGE_SIZE,
+        DEFAULT_IMAGE_SIZE,
+    )  # see openpi `preprocessing_pytorch.py`
 
     # Add empty images. Used to add empty cameras when no image features are present.
     empty_cameras: int = 0

src/lerobot/policies/pi05/modeling_pi05.py

@@ -41,7 +41,7 @@ else:
     PaliGemmaForConditionalGeneration = None
 
 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.policies.pi05.configuration_pi05 import PI05Config
+from lerobot.policies.pi05.configuration_pi05 import DEFAULT_IMAGE_SIZE, PI05Config
 from lerobot.policies.pretrained import PreTrainedPolicy, T
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor
 from lerobot.utils.constants import (
@@ -336,6 +336,7 @@ class PaliGemmaWithExpertModel(
         action_expert_config,
         use_adarms=None,
         precision: Literal["bfloat16", "float32"] = "bfloat16",
+        image_size: int = DEFAULT_IMAGE_SIZE,
     ):
         if use_adarms is None:
             use_adarms = [False, False]
@@ -355,6 +356,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.text_config.vocab_size = 257152
         vlm_config_hf.text_config.use_adarms = use_adarms[0]
         vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
+        vlm_config_hf.vision_config.image_size = image_size
         vlm_config_hf.vision_config.intermediate_size = 4304
         vlm_config_hf.vision_config.projection_dim = 2048
         vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
@@ -518,11 +520,17 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         paligemma_config = get_gemma_config(config.paligemma_variant)
         action_expert_config = get_gemma_config(config.action_expert_variant)
 
+        if config.image_resolution[0] != config.image_resolution[1]:
+            raise ValueError(
+                f"PaliGemma expects square image resolution, invalid resolution: {config.image_resolution}"
+            )
+
         self.paligemma_with_expert = PaliGemmaWithExpertModel(
             paligemma_config,
             action_expert_config,
             use_adarms=[False, True],
             precision=config.dtype,
+            image_size=config.image_resolution[0],
         )
 
         self.action_in_proj = nn.Linear(config.max_action_dim, action_expert_config.width)
@@ -538,6 +546,8 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         if config.compile_model:
             torch.set_float32_matmul_precision("high")
             self.sample_actions = torch.compile(self.sample_actions, mode=config.compile_mode)
+            # Also compile the main forward pass used during training
+            self.forward = torch.compile(self.forward, mode=config.compile_mode)
 
         msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
 
@@ -785,16 +795,13 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         )
 
         dt = -1.0 / num_steps
-        dt = torch.tensor(dt, dtype=torch.float32, device=device)
 
         x_t = noise
-        time = torch.tensor(1.0, dtype=torch.float32, device=device)
-        while time >= -dt / 2:
-            expanded_time = time.expand(bsize)
+        for step in range(num_steps):
+            time = 1.0 + step * dt
+            time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)
 
-            # Define a closure function to properly capture expanded_time
-            # This avoids the lambda expression (E731) and loop variable binding (B023) issues
-            def denoise_step_partial_call(input_x_t, current_timestep=expanded_time):
+            def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
                 return self.denoise_step(
                     prefix_pad_masks=prefix_pad_masks,
                     past_key_values=past_key_values,
@@ -818,15 +825,11 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             else:
                 v_t = denoise_step_partial_call(x_t)
 
-            # Euler step
-            x_t += dt * v_t
+            x_t = x_t + dt * v_t
 
-            # Record x_t and v_t after Euler step
             if self.rtc_processor is not None and self.rtc_processor.is_debug_enabled():
                 self.rtc_processor.track(time=time, x_t=x_t, v_t=v_t)
 
-            time += dt
-
         return x_t
 
     def denoise_step(