faster canbus

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

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

.github/workflows/documentation.yml

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

.github/workflows/fast_tests.yml

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

.github/workflows/full_tests.yml

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

.github/workflows/nightly.yml

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

.github/workflows/release.yml

@@ -29,7 +29,6 @@ jobs:
   build-and-publish:
     name: Build and publish Python distributions
     runs-on: ubuntu-latest
-    if: github.repository == 'huggingface/lerobot'
     outputs:
       version: ${{ steps.extract_info.outputs.tag_version }}
     permissions:
@@ -84,11 +83,11 @@ jobs:
           fi
 
       - name: Remove Tags with Git dependencies
-        # TODO(Steven): Temporary patch to remove pi from PyPi 0.4.0 release due to its reliance on git dependencies.
+        # TODO(Steven): Temporary patch to remove libero and pi from PyPi 0.4.0 release due to its reliance on git dependencies.
         run: |
           echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
-          grep -E '@ git\+https|lerobot\[pi\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
-          sed -E -i '/@ git\+https|lerobot\[pi\]/d' pyproject.toml
+          grep -E '@ git\+https|lerobot\[pi\]|lerobot\[libero\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
+          sed -E -i '/@ git\+https|lerobot\[pi\]|lerobot\[libero\]/d' pyproject.toml
           echo "::info:: Git dependencies removed. Proceeding with build."
 
       - name: Install build dependencies

.github/workflows/stale.yml

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

.github/workflows/unbound_deps_tests.yml

@@ -43,22 +43,14 @@ jobs:
   full-tests:
     name: Full Unbound Tests
     runs-on: ubuntu-latest
-    if: github.repository == 'huggingface/lerobot'
     env:
       MUJOCO_GL: egl
-      HF_HOME: /mnt/cache/.cache/huggingface
-      HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
     steps:
       - uses: actions/checkout@v4
         with:
           lfs: true
           persist-credentials: false
 
-      # NOTE(Steven): Mount to `/mnt` to avoid the limited storage on `/home`. Consider cleaning default SDKs or using self-hosted runners for more space.
-      # (As of 2024-06-10, the runner's `/home` has only 6.2 GB free—8% of its 72 GB total.)
-      - name: Setup /mnt storage
-        run: sudo chown -R $USER:$USER /mnt
-
       - name: Install apt dependencies
         run: |
           sudo apt-get update && sudo apt-get install -y build-essential \
@@ -78,7 +70,7 @@ jobs:
           echo "Dependencies unbound:" && cat pyproject.toml
 
       - name: Install lerobot with all extras
-        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
+        run: uv sync --all-extras
 
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv

README.md

@@ -186,7 +186,7 @@ For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
 > [!NOTE]
-> For lerobot 0.4.0, if you want to install pi tags, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+> For lerobot 0.4.0, if you want to install libero or pi tags, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`.
 >
 > This will be solved in the next patch release
 

benchmarks/video/benchmark.py

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

benchmarks/video/capture_camera_feed.py

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

benchmarks/video/run_video_benchmark.py

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

docs/source/_toctree.yml

@@ -9,14 +9,14 @@
     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
     title: Train a Robot with RL
   - local: hilserl_sim
     title: Train RL in Simulation
+  - local: async
+    title: Use Async Inference
   - local: multi_gpu_training
     title: Multi GPU training
   title: "Tutorials"
@@ -39,20 +39,10 @@
     title: π₀.₅ (Pi05)
   - local: groot
     title: NVIDIA GR00T N1.5
-  - local: xvla
-    title: X-VLA
   title: "Policies"
 - sections:
-  - local: async
-    title: Use Async Inference
-  - local: rtc
-    title: Real-Time Chunking (RTC)
-  title: "Inference"
-- sections:
-  - local: envhub
-    title: Environments from the Hub
-  - local: envhub_leisaac
-    title: Control & Train Robots in Sim (LeIsaac)
+  - local: il_sim
+    title: Imitation Learning in Sim
   - local: libero
     title: Using Libero
   - local: metaworld
@@ -67,8 +57,6 @@
     title: Implement your own processor
   - local: processors_robots_teleop
     title: Processors for Robots and Teleoperators
-  - local: env_processor
-    title: Environment Processors
   title: "Robot Processors"
 - sections:
   - local: so101
@@ -83,19 +71,11 @@
     title: Hope Jr
   - local: reachy2
     title: Reachy 2
-  - local: unitree_g1
-    title: Unitree G1
-  - local: earthrover_mini_plus
-    title: Earth Rover Mini
   title: "Robots"
 - sections:
   - local: phone_teleop
     title: Phone
   title: "Teleoperators"
-- sections:
-  - local: torch_accelerators
-    title: PyTorch accelerators
-  title: "Supported Hardware"
 - sections:
   - local: notebooks
     title: Notebooks

docs/source/async.mdx

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

docs/source/bring_your_own_policies.mdx

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

docs/source/earthrover_mini_plus.mdx

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

docs/source/env_processor.mdx

@@ -1,418 +0,0 @@
-# Environment Processors
-
-Environment processors are a critical layer in LeRobot's data processing architecture that handle **environment-specific** transformations, separate from policy-specific processing. This separation of concerns enables cleaner code, better modularity, and easier experimentation with different environments and policies.
-
-## Why Environment Processors?
-
-When working with different robot environments (LIBERO, MetaWorld, Aloha, etc.), each environment often has unique data formats, coordinate systems, and conventions that need standardization **before** policy processing. Without environment processors, these transformations would be:
-
-1. **Hardcoded in environment code** - Making it difficult to experiment with different state representations
-2. **Duplicated across policies** - Each policy would need to handle environment-specific quirks
-3. **Mixed with policy logic** - Violating separation of concerns and making debugging harder
-
-Environment processors solve this by providing a **dedicated processing layer** between raw environment observations and policy inputs.
-
-## The Processing Pipeline
-
-Here's how data flows through the complete processing pipeline during evaluation:
-
-```python
-# In lerobot_eval.py rollout() function:
-
-# 1. Raw environment observation (numpy arrays, various formats)
-raw_observation = env.step(action)
-
-# 2. Convert numpy to torch, normalize images [0,1]
-observation = preprocess_observation(raw_observation)
-
-# 3. Add task metadata (for multi-task environments)
-observation = add_envs_task(env, observation)
-
-# 4. ENVIRONMENT-SPECIFIC preprocessing (NEW!)
-#    - Flatten robot states
-#    - Rotate images to match dataset conventions
-#    - Handle environment-specific coordinate systems
-observation = env_preprocessor(observation)
-
-# 5. POLICY-SPECIFIC preprocessing
-#    - Normalize with dataset statistics
-#    - Add batch dimensions
-#    - Move to GPU
-#    - Tokenize language instructions
-observation = preprocessor(observation)
-
-# 6. Policy inference
-action = policy.select_action(observation)
-
-# 7. POLICY-SPECIFIC postprocessing
-#    - Unnormalize actions
-#    - Remove batch dimensions
-action = postprocessor(action)
-
-# 8. ENVIRONMENT-SPECIFIC postprocessing (NEW!)
-#    - Convert action formats if needed
-#    - Apply environment-specific constraints
-action_transition = {"action": action}
-action_transition = env_postprocessor(action_transition)
-action = action_transition["action"]
-
-# 9. Execute in environment
-env.step(action)
-```
-
-## The Benefits
-
-### 1. **Separation of Concerns**
-
-Environment processors handle transformations specific to the **environment's data format**, while policy processors handle transformations specific to the **model's requirements**.
-
-```python
-# ❌ Before: Mixed concerns
-class LiberoVLAPolicy:
-    def preprocess(self, obs):
-        # Environment-specific: Flatten robot state (shouldn't be in policy!)
-        state = self._flatten_robot_state(obs["robot_state"])
-        # Policy-specific: Normalize with dataset stats
-        state = self.normalizer(state)
-        return state
-
-# ✅ After: Clear separation
-# Environment processor: Handles LIBERO's nested robot state
-env_preprocessor = LiberoProcessorStep()  # Flattens robot_state
-
-# Policy processor: Handles model requirements
-policy_preprocessor = NormalizerProcessorStep(stats=dataset_stats)
-```
-
-### 2. **Flexibility and Reusability**
-
-The same policy can work with different environment processors, and the same environment processor can work with different policies:
-
-```python
-# Use SmolVLA policy with LIBERO environment
-libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
-smolvla_preprocessor, smolvla_postprocessor = make_pre_post_processors(smolvla_cfg)
-
-# Or use ACT policy with the same LIBERO environment
-libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
-act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
-```
-
-### 3. **Easier Experimentation**
-
-Want to try different state representations for LIBERO? Just create a new processor:
-
-```python
-# Original: 8D state (pos + quat→axisangle + gripper)
-@ProcessorStepRegistry.register("libero_processor")
-class LiberoProcessorStep(ObservationProcessorStep):
-    def _process_observation(self, obs):
-        eef_pos = robot_state["eef"]["pos"]          # 3D
-        eef_axisangle = quat2axisangle(quat)         # 3D
-        gripper = robot_state["gripper"]["qpos"]     # 2D
-        state = torch.cat([eef_pos, eef_axisangle, gripper], dim=-1)  # 8D
-        return state
-
-# Experiment: Add velocity for better control
-@ProcessorStepRegistry.register("libero_velocity_processor")
-class LiberoVelocityProcessorStep(ObservationProcessorStep):
-    def _process_observation(self, obs):
-        # Include velocities for 14D state
-        eef_pos = robot_state["eef"]["pos"]          # 3D
-        eef_axisangle = quat2axisangle(quat)         # 3D
-        eef_vel = robot_state["eef"]["vel"]          # 3D  (NEW)
-        gripper_pos = robot_state["gripper"]["qpos"] # 2D
-        gripper_vel = robot_state["gripper"]["qvel"] # 3D  (NEW)
-        state = torch.cat([eef_pos, eef_axisangle, eef_vel,
-                          gripper_pos, gripper_vel], dim=-1)  # 14D
-        return state
-```
-
-### 4. **Cleaner Environment Code**
-
-Environments expose **all available data** without needing to know what downstream models will use:
-
-```python
-# LIBERO environment exposes full robot state
-observation = {
-    "pixels": {"image": img, "image2": img2},
-    "robot_state": {
-        "eef": {"pos": ..., "quat": ..., "vel": ..., "mat": ..., "axisangle": ...},
-        "gripper": {"qpos": ..., "qvel": ...},
-        "joints": {"pos": ..., "vel": ...}
-    }
-}
-
-# Environment processor decides what to use
-# Policy processor handles model-specific transformations
-```
-
-## Using Environment Processors
-
-### Factory Function
-
-The `make_env_pre_post_processors` function follows the same pattern as `make_pre_post_processors` for policies:
-
-```python
-from lerobot.envs.factory import make_env_pre_post_processors
-from lerobot.envs.configs import LiberoEnv, PushtEnv
-
-# For LIBERO: Returns LiberoProcessorStep in preprocessor
-libero_cfg = LiberoEnv(task="libero_spatial", camera_name=["agentview"])
-env_preprocessor, env_postprocessor = make_env_pre_post_processors(libero_cfg)
-
-# For other environments: Returns identity processors (no-op)
-pusht_cfg = PushtEnv()
-env_preprocessor, env_postprocessor = make_env_pre_post_processors(pusht_cfg)
-```
-
-### Implementation in `envs/factory.py`
-
-```python
-def make_env_pre_post_processors(
-    env_cfg: EnvConfig,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-]:
-    """
-    Create preprocessor and postprocessor pipelines for environment observations.
-
-    Args:
-        env_cfg: The configuration of the environment.
-
-    Returns:
-        A tuple containing:
-            - preprocessor: Pipeline that processes environment observations
-            - postprocessor: Pipeline that processes environment outputs
-    """
-    # For LIBERO environments, add the LiberoProcessorStep to preprocessor
-    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
-        preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
-    else:
-        # For all other environments, return an identity preprocessor
-        preprocessor = PolicyProcessorPipeline(steps=[])
-
-    # Postprocessor is currently identity for all environments
-    # Future: Could add environment-specific action transformations
-    postprocessor = PolicyProcessorPipeline(steps=[])
-
-    return preprocessor, postprocessor
-```
-
-### Integration in Evaluation
-
-In `lerobot_eval.py`, the environment processors are created once and used throughout:
-
-```python
-def eval_main(cfg: EvalPipelineConfig):
-    # Create environment
-    envs = make_env(cfg.env, n_envs=cfg.eval.batch_size)
-
-    # Create policy
-    policy = make_policy(cfg=cfg.policy, env_cfg=cfg.env)
-
-    # Create policy processors
-    preprocessor, postprocessor = make_pre_post_processors(
-        policy_cfg=cfg.policy,
-        pretrained_path=cfg.policy.pretrained_path,
-    )
-
-    # Create environment processors (NEW!)
-    env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env)
-
-    # Run evaluation with both processor types
-    eval_policy_all(
-        envs=envs,
-        policy=policy,
-        env_preprocessor=env_preprocessor,      # Environment-specific
-        env_postprocessor=env_postprocessor,    # Environment-specific
-        preprocessor=preprocessor,              # Policy-specific
-        postprocessor=postprocessor,            # Policy-specific
-        n_episodes=cfg.eval.n_episodes,
-    )
-```
-
-## Example: LIBERO Environment Processor
-
-The `LiberoProcessorStep` demonstrates a real-world environment processor:
-
-```python
-from lerobot.processor.pipeline import ObservationProcessorStep
-
-@dataclass
-@ProcessorStepRegistry.register(name="libero_processor")
-class LiberoProcessorStep(ObservationProcessorStep):
-    """
-    Processes LIBERO observations into the LeRobot format.
-
-    **State Processing:**
-    - Extracts end-effector position (3D)
-    - Converts quaternion to axis-angle representation (3D)
-    - Extracts gripper joint positions (2D)
-    - Concatenates into 8D state vector
-
-    **Image Processing:**
-    - Rotates images 180° to match HuggingFaceVLA/libero convention
-    """
-
-    def _process_observation(self, observation):
-        processed_obs = observation.copy()
-
-        # Process images: Flip 180° for camera convention
-        for key in list(processed_obs.keys()):
-            if key.startswith("observation.images."):
-                img = processed_obs[key]
-                img = torch.flip(img, dims=[2, 3])  # Flip H and W
-                processed_obs[key] = img
-
-        # Process robot_state: Flatten to 8D vector
-        if "observation.robot_state" in processed_obs:
-            robot_state = processed_obs.pop("observation.robot_state")
-
-            eef_pos = robot_state["eef"]["pos"]           # (B, 3)
-            eef_quat = robot_state["eef"]["quat"]         # (B, 4)
-            gripper_qpos = robot_state["gripper"]["qpos"] # (B, 2)
-
-            # Convert quaternion to axis-angle
-            eef_axisangle = self._quat2axisangle(eef_quat)  # (B, 3)
-
-            # Concatenate into single state vector
-            state = torch.cat((eef_pos, eef_axisangle, gripper_qpos), dim=-1)
-            state = state.float()
-
-            processed_obs["observation.state"] = state
-
-        return processed_obs
-```
-
-### Why These Transformations?
-
-1. **Image Rotation**: The HuggingFaceVLA/libero dataset has images rotated 180° from the raw LIBERO simulator. The processor handles this convention mismatch so policies trained on the dataset work seamlessly.
-
-2. **State Flattening**: The raw LIBERO environment exposes nested dictionaries with all available state information (position, quaternion, velocity, matrix representation, etc.). The processor:
-   - Selects the relevant components (pos, quat, gripper)
-   - Converts quaternion to axis-angle (more suitable for learning)
-   - Flattens to a single 8D vector that policies expect
-
-3. **Flexibility**: The environment still exposes **all** raw data. If you want to try different state representations (e.g., including velocities, using matrix representation instead of axis-angle), you can create a new processor without modifying the environment code.
-
-## Adding Environment Processors for New Environments
-
-To add environment processors for a new environment:
-
-### 1. Create the Processor Step
-
-```python
-# In src/lerobot/processor/env_processor.py
-
-@dataclass
-@ProcessorStepRegistry.register(name="myenv_processor")
-class MyEnvProcessorStep(ObservationProcessorStep):
-    """Process observations from MyEnv."""
-
-    def _process_observation(self, observation):
-        processed = observation.copy()
-
-        # Your environment-specific transformations
-        if "myenv.specific.state" in processed:
-            state = processed.pop("myenv.specific.state")
-            # Transform to standard format
-            processed["observation.state"] = self._transform_state(state)
-
-        return processed
-```
-
-### 2. Update the Factory
-
-```python
-# In src/lerobot/envs/factory.py
-
-def make_env_pre_post_processors(env_cfg: EnvConfig):
-    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
-        preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
-    elif isinstance(env_cfg, MyEnvConfig) or "myenv" in env_cfg.type:
-        preprocessor = PolicyProcessorPipeline(steps=[MyEnvProcessorStep()])
-    else:
-        preprocessor = PolicyProcessorPipeline(steps=[])
-
-    postprocessor = PolicyProcessorPipeline(steps=[])
-    return preprocessor, postprocessor
-```
-
-### 3. Use in Evaluation
-
-No changes needed! The evaluation script automatically uses the appropriate processor:
-
-```bash
-lerobot-eval \
-    --policy.path=lerobot/my_policy \
-    --env.type=myenv \  # Automatically uses MyEnvProcessorStep
-    --eval.n_episodes=10
-```
-
-## Future: Environment Postprocessors
-
-Currently, postprocessors are identity (no-op) for all environments. Future use cases include:
-
-### Action Space Transformations
-
-```python
-@dataclass
-class MyEnvActionPostprocessor(ProcessorStep):
-    """Convert policy actions to environment-specific format."""
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        action = transition["action"]
-
-        # Example: Convert from Cartesian to joint space
-        if self.action_space == "joint":
-            action = self.ik_solver(action)
-
-        # Example: Apply environment-specific safety limits
-        action = torch.clamp(action, self.min_action, self.max_action)
-
-        transition["action"] = action
-        return transition
-```
-
-### Coordinate System Conversions
-
-```python
-@dataclass
-class CoordinateTransformPostprocessor(ProcessorStep):
-    """Transform actions between coordinate systems."""
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        action = transition["action"]
-
-        # Example: Policy outputs in world frame, env expects base frame
-        action = self.world_to_base_transform(action)
-
-        transition["action"] = action
-        return transition
-```
-
-## Best Practices
-
-1. **Keep environment processors simple**: They should only handle environment-specific data format issues, not complex learning-related transformations.
-
-2. **Use policy processors for model requirements**: Normalization, batching, device placement, and tokenization belong in policy processors.
-
-3. **Expose all data from environments**: Let processors decide what to use rather than hardcoding choices in the environment.
-
-4. **Document conventions**: Clearly document any coordinate system conventions, camera orientations, or data formats that your processor handles.
-
-5. **Test independently**: Environment processors should be testable without loading full policies or environments.
-
-## Summary
-
-Environment processors provide a **clean separation** between environment-specific data transformations and policy-specific model requirements. This architecture:
-
-- ✅ Enables easy experimentation with different state representations
-- ✅ Allows policies to work seamlessly across different environments
-- ✅ Keeps environment code focused on simulation/hardware interface
-- ✅ Makes processor pipelines more maintainable and debuggable
-- ✅ Follows the single responsibility principle
-
-The key insight: **Environments define data formats, processors standardize them, policies consume standardized data.** Each layer has a clear, focused responsibility.

docs/source/envhub.mdx

@@ -1,424 +0,0 @@
-# Loading Environments from the Hub
-
-The **EnvHub** feature allows you to load simulation environments directly from the Hugging Face Hub with a single line of code. This unlocks a powerful new model for collaboration: instead of environments being locked away inside monolithic libraries, anyone can publish custom environments and share them with the community.
-
-## Overview
-
-With EnvHub, you can:
-
-- Load environments from the Hub instantly
-- Share your custom simulation tasks with the community
-- Version control your environments using Git
-- Distribute complex physics simulations without packaging hassles
-
-## Quick Start
-
-Loading an environment from the Hub is as simple as:
-
-```python
-from lerobot.envs.factory import make_env
-
-# Load a hub environment (requires explicit consent to run remote code)
-env = make_env("lerobot/cartpole-env", trust_remote_code=True)
-```
-
-<Tip warning={true}>
-  **Security Notice**: Loading environments from the Hub executes Python code
-  from third-party repositories. Only use `trust_remote_code=True` with
-  repositories you trust. We strongly recommend pinning to a specific commit
-  hash for reproducibility and security.
-</Tip>
-
-## What is EnvHub?
-
-EnvHub is a framework that allows researchers and developers to:
-
-1. **Publish environments** to the Hugging Face Hub as Git repositories
-2. **Load environments** dynamically without installing them as packages
-3. **Version and track** environment changes using Git semantics
-4. **Discover** new simulation tasks shared by the community
-
-This design means you can go from discovering an interesting environment on the Hub to running experiments in seconds, without worrying about dependency conflicts or complex installation procedures.
-
-## Repository Structure
-
-To make your environment loadable from the Hub, your repository must contain at minimum:
-
-### Required Files
-
-**`env.py`** (or custom Python file)
-
-- Must expose a `make_env(n_envs: int, use_async_envs: bool)` function
-- This function should return one of:
-  - A `gym.vector.VectorEnv` (most common)
-  - A single `gym.Env` (will be automatically wrapped)
-  - A dict mapping `{suite_name: {task_id: VectorEnv}}` (for multi-task benchmarks)
-
-### Optional Files
-
-**`requirements.txt`**
-
-- List any additional dependencies your environment needs
-- Users will need to install these manually before loading your environment
-
-**`README.md`**
-
-- Document your environment: what task it implements, observation/action spaces, rewards, etc.
-- Include usage examples and any special setup instructions
-
-**`.gitignore`**
-
-- Exclude unnecessary files from your repository
-
-### Example Repository Structure
-
-```
-my-environment-repo/
-├── env.py                 # Main environment definition (required)
-├── requirements.txt       # Dependencies (optional)
-├── README.md             # Documentation (recommended)
-├── assets/               # Images, videos, etc. (optional)
-│   └── demo.gif
-└── configs/              # Config files if needed (optional)
-    └── task_config.yaml
-```
-
-## Creating Your Environment Repository
-
-### Step 1: Define Your Environment
-
-Create an `env.py` file with a `make_env` function:
-
-```python
-# env.py
-import gymnasium as gym
-
-def make_env(n_envs: int = 1, use_async_envs: bool = False):
-    """
-    Create vectorized environments for your custom task.
-
-    Args:
-        n_envs: Number of parallel environments
-        use_async_envs: Whether to use AsyncVectorEnv or SyncVectorEnv
-
-    Returns:
-        gym.vector.VectorEnv or dict mapping suite names to vectorized envs
-    """
-    def _make_single_env():
-        # Create your custom environment
-        return gym.make("CartPole-v1")
-
-    # Choose vector environment type
-    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
-
-    # Create vectorized environment
-    vec_env = env_cls([_make_single_env for _ in range(n_envs)])
-
-    return vec_env
-```
-
-### Step 2: Test Locally
-
-Before uploading, test your environment locally:
-
-```python
-from lerobot.envs.utils import _load_module_from_path, _call_make_env, _normalize_hub_result
-
-# Load your module
-module = _load_module_from_path("./env.py")
-
-# Test the make_env function
-result = _call_make_env(module, n_envs=2, use_async_envs=False)
-normalized = _normalize_hub_result(result)
-
-# Verify it works
-suite_name = next(iter(normalized))
-env = normalized[suite_name][0]
-obs, info = env.reset()
-print(f"Observation shape: {obs.shape if hasattr(obs, 'shape') else type(obs)}")
-env.close()
-```
-
-### Step 3: Upload to the Hub
-
-Upload your repository to Hugging Face:
-
-```bash
-# Install huggingface_hub if needed
-pip install huggingface_hub
-
-# Login to Hugging Face
-huggingface-cli login
-
-# Create a new repository
-huggingface-cli repo create my-custom-env --type space --org my-org
-
-# Initialize git and push
-git init
-git add .
-git commit -m "Initial environment implementation"
-git remote add origin https://huggingface.co/my-org/my-custom-env
-git push -u origin main
-```
-
-Alternatively, use the `huggingface_hub` Python API:
-
-```python
-from huggingface_hub import HfApi
-
-api = HfApi()
-
-# Create repository
-api.create_repo("my-custom-env", repo_type="space")
-
-# Upload files
-api.upload_folder(
-    folder_path="./my-env-folder",
-    repo_id="username/my-custom-env",
-    repo_type="space",
-)
-```
-
-## Loading Environments from the Hub
-
-### Basic Usage
-
-```python
-from lerobot.envs.factory import make_env
-
-# Load from the hub
-envs_dict = make_env(
-    "username/my-custom-env",
-    n_envs=4,
-    trust_remote_code=True
-)
-
-# Access the environment
-suite_name = next(iter(envs_dict))
-env = envs_dict[suite_name][0]
-
-# Use it like any gym environment
-obs, info = env.reset()
-action = env.action_space.sample()
-obs, reward, terminated, truncated, info = env.step(action)
-```
-
-### Advanced: Pinning to Specific Versions
-
-For reproducibility and security, pin to a specific Git revision:
-
-```python
-# Pin to a specific branch
-env = make_env("username/my-env@main", trust_remote_code=True)
-
-# Pin to a specific commit (recommended for papers/experiments)
-env = make_env("username/my-env@abc123def456", trust_remote_code=True)
-
-# Pin to a tag
-env = make_env("username/my-env@v1.0.0", trust_remote_code=True)
-```
-
-### Custom File Paths
-
-If your environment definition is not in `env.py`:
-
-```python
-# Load from a custom file
-env = make_env("username/my-env:custom_env.py", trust_remote_code=True)
-
-# Combine with version pinning
-env = make_env("username/my-env@v1.0:envs/task_a.py", trust_remote_code=True)
-```
-
-### Async Environments
-
-For better performance with multiple environments:
-
-```python
-envs_dict = make_env(
-    "username/my-env",
-    n_envs=8,
-    use_async_envs=True,  # Use AsyncVectorEnv for parallel execution
-    trust_remote_code=True
-)
-```
-
-## URL Format Reference
-
-The hub URL format supports several patterns:
-
-| Pattern              | Description                    | Example                                |
-| -------------------- | ------------------------------ | -------------------------------------- |
-| `user/repo`          | Load `env.py` from main branch | `make_env("lerobot/pusht-env")`        |
-| `user/repo@revision` | Load from specific revision    | `make_env("lerobot/pusht-env@main")`   |
-| `user/repo:path`     | Load custom file               | `make_env("lerobot/envs:pusht.py")`    |
-| `user/repo@rev:path` | Revision + custom file         | `make_env("lerobot/envs@v1:pusht.py")` |
-
-## Multi-Task Environments
-
-For benchmarks with multiple tasks (like LIBERO), return a nested dictionary:
-
-```python
-def make_env(n_envs: int = 1, use_async_envs: bool = False):
-    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
-
-    # Return dict: {suite_name: {task_id: VectorEnv}}
-    return {
-        "suite_1": {
-            0: env_cls([lambda: gym.make("Task1-v0") for _ in range(n_envs)]),
-            1: env_cls([lambda: gym.make("Task2-v0") for _ in range(n_envs)]),
-        },
-        "suite_2": {
-            0: env_cls([lambda: gym.make("Task3-v0") for _ in range(n_envs)]),
-        }
-    }
-```
-
-## Security Considerations
-
-<Tip warning={true}>
-  **Important**: The `trust_remote_code=True` flag is required to execute
-  environment code from the Hub. This is by design for security.
-</Tip>
-
-When loading environments from the Hub:
-
-1. **Review the code first**: Visit the repository and inspect `env.py` before loading
-2. **Pin to commits**: Use specific commit hashes for reproducibility
-3. **Check dependencies**: Review `requirements.txt` for suspicious packages
-4. **Use trusted sources**: Prefer official organizations or well-known researchers
-5. **Sandbox if needed**: Run untrusted code in isolated environments (containers, VMs)
-
-Example of safe usage:
-
-```python
-# ❌ BAD: Loading without inspection
-env = make_env("random-user/untrusted-env", trust_remote_code=True)
-
-# ✅ GOOD: Review code, then pin to specific commit
-# 1. Visit https://huggingface.co/trusted-org/verified-env
-# 2. Review the env.py file
-# 3. Copy the commit hash
-env = make_env("trusted-org/verified-env@a1b2c3d4", trust_remote_code=True)
-```
-
-## Example: CartPole from the Hub
-
-Here's a complete example using the reference CartPole environment:
-
-```python
-from lerobot.envs.factory import make_env
-import numpy as np
-
-# Load the environment
-envs_dict = make_env("lerobot/cartpole-env", n_envs=4, trust_remote_code=True)
-
-# Get the vectorized environment
-suite_name = next(iter(envs_dict))
-env = envs_dict[suite_name][0]
-
-# Run a simple episode
-obs, info = env.reset()
-done = np.zeros(env.num_envs, dtype=bool)
-total_reward = np.zeros(env.num_envs)
-
-while not done.all():
-    # Random policy
-    action = env.action_space.sample()
-    obs, reward, terminated, truncated, info = env.step(action)
-    total_reward += reward
-    done = terminated | truncated
-
-print(f"Average reward: {total_reward.mean():.2f}")
-env.close()
-```
-
-## Benefits of EnvHub
-
-### For Environment Authors
-
-- **Easy distribution**: No PyPI packaging required
-- **Version control**: Use Git for environment versioning
-- **Rapid iteration**: Push updates instantly
-- **Documentation**: Hub README renders beautifully
-- **Community**: Reach LeRobot users directly
-
-### For Researchers
-
-- **Quick experiments**: Load any environment in one line
-- **Reproducibility**: Pin to specific commits
-- **Discovery**: Browse environments on the Hub
-- **No conflicts**: No need to install conflicting packages
-
-### For the Community
-
-- **Growing ecosystem**: More diverse simulation tasks
-- **Standardization**: Common `make_env` API
-- **Collaboration**: Fork and improve existing environments
-- **Accessibility**: Lower barrier to sharing research
-
-## Troubleshooting
-
-### "Refusing to execute remote code"
-
-You must explicitly pass `trust_remote_code=True`:
-
-```python
-env = make_env("user/repo", trust_remote_code=True)
-```
-
-### "Module X not found"
-
-The hub environment has dependencies you need to install:
-
-```bash
-# Check the repo's requirements.txt and install dependencies
-pip install gymnasium numpy
-```
-
-### "make_env not found in module"
-
-Your `env.py` must expose a `make_env` function:
-
-```python
-def make_env(n_envs: int, use_async_envs: bool):
-    # Your implementation
-    pass
-```
-
-### Environment returns wrong type
-
-The `make_env` function must return:
-
-- A `gym.vector.VectorEnv`, or
-- A single `gym.Env`, or
-- A dict `{suite_name: {task_id: VectorEnv}}`
-
-## Best Practices
-
-1. **Document your environment**: Include observation/action space descriptions, reward structure, and termination conditions in your README
-2. **Add requirements.txt**: List all dependencies with versions
-3. **Test thoroughly**: Verify your environment works locally before pushing
-4. **Use semantic versioning**: Tag releases with version numbers
-5. **Add examples**: Include usage examples in your README
-6. **Keep it simple**: Minimize dependencies when possible
-7. **License your work**: Add a LICENSE file to clarify usage terms
-
-## Future Directions
-
-The EnvHub ecosystem enables exciting possibilities:
-
-- **GPU-accelerated physics**: Share Isaac Gym or Brax environments
-- **Photorealistic rendering**: Distribute environments with advanced graphics
-- **Multi-agent scenarios**: Complex interaction tasks
-- **Real-world simulators**: Digital twins of physical setups
-- **Procedural generation**: Infinite task variations
-- **Domain randomization**: Pre-configured DR pipelines
-
-As more researchers and developers contribute, the diversity and quality of available environments will grow, benefiting the entire robotics learning community.
-
-## See Also
-
-- [Hugging Face Hub Documentation](https://huggingface.co/docs/hub/en/index)
-- [Gymnasium Documentation](https://gymnasium.farama.org/index.html)
-- [Example Hub Environment](https://huggingface.co/lerobot/cartpole-env)

docs/source/envhub_leisaac.mdx

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

docs/source/groot.mdx

@@ -40,7 +40,7 @@ python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} i
 3. Install LeRobot by running:
 
 ```bash
-pip install lerobot[groot]
+pip install lerobot[groot] # consider also installing libero,dev and test tags
 ```
 
 ## Usage
@@ -83,9 +83,6 @@ accelerate launch \
 
 ### Libero Benchmark Results
 
-> [!NOTE]
-> Follow our instructions for Libero usage: [Libero](./libero)
-
 GR00T has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the GR00T N1.5 model for 30k steps on the Libero dataset and compared the results to the GR00T reference results.
 
 | Benchmark          | LeRobot Implementation | GR00T Reference |

docs/source/il_robots.mdx

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

docs/source/il_sim.mdx

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

docs/source/installation.mdx

@@ -82,7 +82,7 @@ For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
 > [!NOTE]
-> For lerobot 0.4.0, if you want to install pi, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`
+> For lerobot 0.4.0, if you want to install libero or pi, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`
 
 ### Troubleshooting
 
@@ -90,7 +90,7 @@ If you encounter build errors, you may need to install additional dependencies:
 To install these for linux run:
 
 ```bash
-sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev
+sudo apt-get install cmake build-essential python-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config
 ```
 
 For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)

docs/source/libero.mdx

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

docs/source/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/pi0.mdx

@@ -28,11 +28,6 @@ As described by Physical Intelligence, while AI has achieved remarkable success
    pip install -e ".[pi]"
    ```
 
-   > [!NOTE]
-   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
-   >
-   > This will be solved in the next patch release
-
 ## Training Data and Capabilities
 
 π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:

docs/source/pi05.mdx

@@ -36,11 +36,6 @@ This diverse training mixture creates a "curriculum" that enables generalization
    pip install -e ".[pi]"
    ```
 
-   > [!NOTE]
-   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
-   >
-   > This will be solved in the next patch release
-
 ## Usage
 
 To use π₀.₅ in your LeRobot configuration, specify the policy type as:

docs/source/rtc.mdx

@@ -1,188 +0,0 @@
-# Real-Time Chunking (RTC)
-
-Real-Time Chunking (RTC) is an inference-time method that allows large, flow-matching based robotic policies, such as [Pi0](./pi0), [Pi0.5](./pi05), and [SmolVLA](./smolvla), to produce smooth, continuous, and reactive motion despite having high inference latency.
-
-These policies generate chunks of future actions (e.g., 50 steps at a time) instead of single actions.
-Because the models are large, producing each chunk takes longer than the time it takes the robot to execute it.
-Naively executing chunks leads to problems such as pauses, jerky transitions, or sudden changes in strategy whenever the next chunk arrives late or disagrees with the previously executed actions.
-
-RTC solves this by asynchronously generating the next chunk while the robot continues executing the current one, and by guiding the new chunk so it aligns smoothly with the portion of the previous chunk that has already been executed.
-
-## How RTC Works (simplified)
-
-RTC lets the robot think ahead while it’s still moving. When the robot is carrying out one chunk of actions, RTC starts creating the next chunk early.
-But since the robot has already moved a bit by the time the new chunk is ready, RTC has to make sure the new chunk still lines up smoothly with what the robot is currently doing.
-
-To do this, RTC treats the beginning of the new chunk like an inpainting or “fill-in-the-gaps” problem:
-it gently adjusts the first part of the new chunk so it blends naturally with the robot’s ongoing motion. The result is no pauses, no sudden jumps.
-
-In technical terms, RTC adds a guidance term to the flow-matching denoising process that forces the overlapping timesteps of the new chunk to stay close to the executed portion of the previous chunk, typically using a soft transition mask.
-
-## Quick Start
-
-### Installation
-
-RTC is built into LeRobot. Just install the policy dependencies you need:
-
-```bash
-# For Pi0 or Pi0.5
-pip install -e ".[pi]"
-
-# For SmolVLA
-pip install -e ".[smolvla]"
-```
-
-### Using RTC with Pi0
-
-You can find a complete reference implementation in [eval_with_real_robot.py](examples/rtc/eval_with_real_robot.py).
-The snippet below provides a simplified pseudo-example of how RTC operates with Pi0 in your pipeline:
-
-```python
-from lerobot.policies.pi0 import PI0Policy, PI0Config
-from lerobot.configs.types import RTCAttentionSchedule
-from lerobot.policies.rtc.configuration_rtc import RTCConfig
-from lerobot.policies.rtc.action_queue import ActionQueue
-
-# Load Pi0 with RTC enabled
-policy_cfg = PI0Config()
-
-# Enable RTC
-policy_cfg.rtc_config = RTCConfig(
-    enabled=True,
-    execution_horizon=10,  # How many steps to blend with previous chunk
-    max_guidance_weight=10.0,  # How strongly to enforce consistency
-    prefix_attention_schedule=RTCAttentionSchedule.EXP,  # Exponential blend
-)
-
-# Load the policy
-policy = PI0Policy.from_pretrained("lerobot/pi0_base", policy_cfg=policy_cfg, device="cuda")
-
-# Now use predict_action_chunk with RTC parameters
-inference_delay = 4  # How many steps of inference latency, this values should be calculated based on the inference latency of the policy
-
-# Initialize the action queue
-action_queue = ActionQueue(policy_cfg.rtc_config)
-
-# Start in a separate thread with the following function
-def get_actions():
-  while True:
-    if should_get_actions:
-
-      prev_actions = action_queue.get_left_over()
-      obs = get_robot_observations(robot)
-
-      # Generate actions WITH RTC
-      actions = policy.predict_action_chunk(
-          obs,
-          inference_delay=inference_delay,
-          prev_chunk_left_over=prev_actions,
-      )
-
-      action_queue.merge(
-          actions, actions, inference_delay
-      )
-
-for step in range(num_steps):
-    action = action_queue.get()
-
-    # Execute the first N actions
-    execute_actions(action)
-```
-
-## Key Parameters
-
-`RTCConfig` has the following parameters to tune:
-
-**`execution_horizon`**: How many timesteps from the previous chunk to maintain consistency with. Higher values mean smoother transitions but potentially less reactivity.
-
-Typical values: 8-12 steps
-
-```python
-RTCConfig(execution_horizon=10)
-```
-
-**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is a optimal value.
-
-**`prefix_attention_schedule`**: How to weight consistency across the overlap region.
-
-- `LINEAR`: Linear decay from inference_delay to execution_horizon
-- `EXP`: Exponential decay (recommended for getting started)
-- `ONES`: Full weight across entire execution_horizon
-- `ZEROS`: Binary (full weight up to inference_delay, then zero)
-
-**`inference_delay`**: How many timesteps of inference latency your system has. This is passed to `predict_action_chunk()` rather than the config, since it may vary at runtime.
-
-## Testing RTC Offline
-
-Before running on a real robot, test RTC with dataset samples to visualize how it works:
-
-```bash
-python examples/rtc/eval_dataset.py \
-    --policy.path=lerobot/pi0_libero_finetuned \
-    --dataset.repo_id=HuggingFaceVLA/libero \
-    --rtc.execution_horizon=10 \
-    --rtc.max_guidance_weight=10.0 \
-    --device=cuda
-```
-
-The script generates a visualization of the denoising process, comparing standard generation (left) with RTC (right). In the RTC plots, you can see how the first few steps (blue/purple lines) are guided to match the red ground truth trajectory (previous chunk's tail), ensuring a smooth transition between chunks.
-
-<p align="center">
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/flow_matching.png"
-    alt="Denoising steps with and without RTC"
-    width="100%"
-  />
-</p>
-
-## Testing RTC with a Real Robot
-
-```bash
-python examples/rtc/eval_with_real_robot.py \
-    --policy.path=${HF_USERNAME}/policy_repo_id \
-    --robot.type=so100_follower \
-    --robot.port=/dev/tty.usbmodem58FA0834591 \
-    --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-    --task="Move green small object into the purple platform" \
-    --duration=120 \
-    --device=cuda
-```
-
-## How It Differs from the Async Inference in LeRobot
-
-Both RTC and [async inference](./async) improve real-time robot control, but they solve different problems.
-
-| Aspect        | Async Inference                                                            | RTC                                                 |
-| ------------- | -------------------------------------------------------------------------- | --------------------------------------------------- |
-| **Problem**   | Idle frames while waiting for inference                                    | Discontinuities between action chunks               |
-| **Solution**  | Decouple prediction from execution                                         | Guide new chunks to continue smoothly from previous |
-| **Benefit**   | No waiting, continuous action                                              | Smooth transitions, natural motion                  |
-| **Best Used** | Async inference is best used with large models with high inference latency | Flow-matching based policies                        |
-
-**Use both together** for maximum smoothness and reactivity!
-
-## Advanced: Debug Tracking
-
-RTC includes built-in debug tracking to help you understand what's happening during inference:
-
-```python
-# Enable debug tracking
-policy_cfg.rtc_config.debug = True
-policy_cfg.rtc_config.debug_maxlen = 100
-
-# After inference, access debug data
-debug_data = policy.rtc_processor.get_debug_data()
-
-# Visualize denoising steps, corrections, etc.
-from lerobot.policies.rtc.debug_visualizer import RTCDebugVisualizer
-visualizer = RTCDebugVisualizer()
-# ... create plots
-```
-
-See `examples/rtc/eval_dataset.py` for a complete example of visualization.
-
-## References
-
-- [Smooth-As-Butter Robot Policies](https://alexander-soare.github.io/robotics/2025/08/05/smooth-as-butter-robot-policies.html) - Excellent technical explanation with real robot results
-- [Physical Intelligence - Real-Time Chunking](https://www.physicalintelligence.company/research/real_time_chunking) - Original paper and research
-- [Kinetix RTC Implementation](https://github.com/Physical-Intelligence/real-time-chunking-kinetix) - Reference implementation from Physical Intelligence

docs/source/so101.mdx

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

docs/source/torch_accelerators.mdx

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

docs/source/unitree_g1.mdx

@@ -1,208 +0,0 @@
-# Unitree G1 Robot Setup and Control
-
-This guide covers the complete setup process for the Unitree G1 humanoid, from initial connection to running gr00t_wbc locomotion.
-
-## About the Unitree G1
-
-We offer support for both 29 and 23 DOF G1. 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

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

docs/source/xvla.mdx

@@ -1,528 +0,0 @@
-# X-VLA: The First Soft-Prompted Robot Foundation Model for Any Robot, Any Task
-
-## Overview
-
-For years, robotics has aspired to build agents that can follow natural human instructions and operate dexterously across many environments and robot bodies. Recent breakthroughs in LLMs and VLMs suggest a path forward: extend these foundation-model architectures to embodied control by grounding them in actions. This has led to the rise of Vision-Language-Action (VLA) models, with the hope that a single generalist model could combine broad semantic understanding with robust manipulation skills.
-
-But training such models is difficult. Robot data is fragmented across platforms, sensors, embodiments, and collection protocols. Heterogeneity appears everywhere: different arm configurations, different action spaces, different camera setups, different visual domains, and different task distributions. These inconsistencies create major distribution shifts that make pretraining unstable and adaptation unreliable.
-
-Inspired by meta-learning and prompt learning, we ask: **"What if a VLA model could learn the structure of each robot and dataset the same way LLMs learn tasks, through prompts?"**
-
-**X-VLA** is a soft-prompted, flow-matching VLA framework that treats each hardware setup as a "task" and encodes it using a small set of learnable embeddings. These **Soft Prompts** capture embodiment and domain-specific variations, guiding the Transformer from the earliest stages of multimodal fusion. With this mechanism, X-VLA can reconcile diverse robot morphologies, data types, and sensor setups within a single unified architecture.
-
-<p align="center">
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-architecture.png"
-    alt="XVLA Architecture"
-    style="max-width: 100%; height: auto; width: 800px;"
-  />
-</p>
-
-Built from pure Transformer encoders, X-VLA scales naturally with model size and dataset diversity. Across 6 simulation benchmarks and 3 real robots, Soft Prompts consistently outperform existing methods in handling hardware and domain differences. X-VLA-0.9B, trained on 290K episodes spanning seven robotic platforms, learns an embodiment-agnostic generalist policy in Phase I, and adapts efficiently to new robots in Phase II simply by learning a new set of prompts, while keeping the backbone frozen.
-
-<p align="center">
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-architecture2.png"
-    alt="XVLA Architecture 2"
-    style="width: 60%; height: auto;"
-  />
-</p>
-
-With only 1% of parameters tuned (9M), X-VLA-0.9B achieves near-π₀ performance on LIBERO and Simpler-WidowX, despite using **300× fewer trainable parameters**. It also demonstrates strong real-world dexterity with minimal demonstrations, including folding cloths in under two minutes.
-
-<p align="center">
-  <img
-    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-fold.png"
-    alt="XVLA fold visualization"
-    style="width: 95%; max-width: 1100px; height: auto;"
-  />
-</p>
-
-X-VLA shows that generalist robot intelligence does not require increasingly complex architectures, only the right way to absorb heterogeneity. Soft Prompts offer a simple, scalable mechanism for unifying diverse robotic data, paving the way toward adaptable, cross-embodiment robot foundation models.
-
-## Installation
-
-After installing LeRobot, install the X-VLA dependencies:
-
-```bash
-pip install -e .[xvla]
-```
-
-After the new release, you'll be able to do:
-
-```bash
-pip install lerobot[xvla]
-```
-
-## Quick Start
-
-### Basic Usage
-
-To use X-VLA in your LeRobot configuration, specify the policy type as:
-
-```bash
-policy.type=xvla
-```
-
-### Evaluating Pre-trained Checkpoints
-
-Example evaluation with LIBERO:
-
-```bash
-lerobot-eval \
-  --policy.path="lerobot/xvla-libero" \
-  --env.type=libero \
-  --env.task=libero_spatial,libero_goal,libero_10 \
-  --env.control_mode=absolute \
-  --eval.batch_size=1 \
-  --eval.n_episodes=1 \
-  --env.episode_length=800 \
-  --seed=142
-```
-
-## Available Checkpoints
-
-### 🎯 Base Model
-
-**[lerobot/xvla-base](https://huggingface.co/lerobot/xvla-base)**
-
-A 0.9B parameter instantiation of X-VLA, trained with a carefully designed data processing and learning recipe. The training pipeline consists of two phases:
-
-- **Phase I: Pretraining** - Pretrained on 290K episodes from Droid, Robomind, and Agibot, spanning seven platforms across five types of robotic arms (single-arm to bi-manual setups). By leveraging soft prompts to absorb embodiment-specific variations, the model learns an embodiment-agnostic generalist policy.
-
-- **Phase II: Domain Adaptation** - Adapted to deployable policies for target domains. A new set of soft prompts is introduced and optimized to encode the hardware configuration of the novel domain, while the pretrained backbone remains frozen.
-
-### Simulation Checkpoints
-
-**[lerobot/xvla-libero](https://huggingface.co/lerobot/xvla-libero)**
-
-Achieves 93% success rate on LIBERO benchmarks. Fine-tuned from the base model for simulation tasks.
-
-**[lerobot/xvla-widowx](https://huggingface.co/lerobot/xvla-widowx)**
-
-Fine-tuned on BridgeData for pick-and-place experiments on compact WidowX platforms. Demonstrates robust manipulation capabilities.
-
-### 🤖 Real-World Checkpoints
-
-**[lerobot/xvla-folding](https://huggingface.co/lerobot/xvla-folding)**
-
-A fine-tuned dexterous manipulation model trained on the high-quality Soft-FOLD cloth folding dataset. Achieves 100% success rate over 2 hours of continuous cloth folding.
-
-**[lerobot/xvla-agibot-world](https://huggingface.co/lerobot/xvla-agibot-world)**
-
-Optimized for AgileX robot dexterous manipulation tasks.
-
-**[lerobot/xvla-google-robot](https://huggingface.co/lerobot/xvla-google-robot)**
-
-Adapted for Google Robot platforms.
-
-## Training X-VLA
-
-### Recommended Training Configuration
-
-When fine-tuning X-VLA for a new embodiment or task, we recommend not freezing the VLM, and also setting the `policy.dtype=bfloat16` to not hit OOM errors.
-
-```bash
-lerobot-train \
-  --dataset.repo_id=YOUR_DATASET \
-  --output_dir=./outputs/xvla_training \
-  --job_name=xvla_training \
-  --policy.path="lerobot/xvla-base" \
-  --policy.repo_id="HF_USER/xvla-your-robot" \
-  --policy.dtype=bfloat16 \
-  --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 precise_sleep
+from lerobot.utils.robot_utils import busy_wait
 from lerobot.utils.utils import (
     init_logging,
     log_say,
@@ -97,7 +97,7 @@ def replay(cfg: ReplayConfig):
         robot.send_action(action)
 
         dt_s = time.perf_counter() - start_episode_t
-        precise_sleep(1 / dataset.fps - dt_s)
+        busy_wait(1 / dataset.fps - dt_s)
 
     robot.disconnect()
 

examples/dataset/load_lerobot_dataset.py

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

examples/lekiwi/evaluate.py

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

examples/lekiwi/record.py

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

examples/lekiwi/replay.py

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

examples/lekiwi/teleoperate.py

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

examples/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/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="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=5.0,  # Safety limit
+)
+
+
+leader_config = OpenArmsLeaderConfig(
+    port_left="can2",   # CAN interface for leader left arm
+    port_right="can3",  # 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/phone_to_so100/evaluate.py

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

examples/phone_to_so100/record.py

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

examples/phone_to_so100/replay.py

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

examples/phone_to_so100/teleoperate.py

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

examples/port_datasets/slurm_aggregate_shards.py

@@ -15,12 +15,16 @@
 # limitations under the License.
 
 import argparse
+import logging
 from pathlib import Path
 
 from datatrove.executor import LocalPipelineExecutor
 from datatrove.executor.slurm import SlurmPipelineExecutor
 from datatrove.pipeline.base import PipelineStep
-from port_droid import DROID_SHARDS
+from port_datasets.droid_rlds.port_droid import DROID_SHARDS
+
+from lerobot.datasets.aggregate import aggregate_datasets
+from lerobot.utils.utils import init_logging
 
 
 class AggregateDatasets(PipelineStep):
@@ -34,11 +38,6 @@ class AggregateDatasets(PipelineStep):
         self.aggr_repo_id = aggregated_repo_id
 
     def run(self, data=None, rank: int = 0, world_size: int = 1):
-        import logging
-
-        from lerobot.datasets.aggregate import aggregate_datasets
-        from lerobot.utils.utils import init_logging
-
         init_logging()
 
         # Since aggregate_datasets already handles parallel processing internally,

examples/port_datasets/slurm_port_shards.py

@@ -20,7 +20,7 @@ from pathlib import Path
 from datatrove.executor import LocalPipelineExecutor
 from datatrove.executor.slurm import SlurmPipelineExecutor
 from datatrove.pipeline.base import PipelineStep
-from port_droid import DROID_SHARDS
+from port_datasets.droid_rlds.port_droid import DROID_SHARDS
 
 
 class PortDroidShards(PipelineStep):
@@ -35,7 +35,7 @@ class PortDroidShards(PipelineStep):
 
     def run(self, data=None, rank: int = 0, world_size: int = 1):
         from datasets.utils.tqdm import disable_progress_bars
-        from port_droid import port_droid, validate_dataset
+        from port_datasets.droid_rlds.port_droid import port_droid, validate_dataset
 
         from lerobot.utils.utils import init_logging
 

examples/port_datasets/slurm_upload.py

@@ -24,7 +24,7 @@ from datatrove.executor.slurm import SlurmPipelineExecutor
 from datatrove.pipeline.base import PipelineStep
 from huggingface_hub import HfApi
 from huggingface_hub.constants import REPOCARD_NAME
-from port_droid import DROID_SHARDS
+from port_datasets.droid_rlds.port_droid import DROID_SHARDS
 
 from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDatasetMetadata
 from lerobot.datasets.utils import create_lerobot_dataset_card
@@ -185,11 +185,11 @@ class UploadDataset(PipelineStep):
 
 
 def make_upload_executor(
-    repo_id, job_name, logs_dir, workers, partition, cpus_per_task, mem_per_cpu, private=False, slurm=True
+    repo_id, job_name, logs_dir, workers, partition, cpus_per_task, mem_per_cpu, slurm=True
 ):
     kwargs = {
         "pipeline": [
-            UploadDataset(repo_id, private=private),
+            UploadDataset(repo_id),
         ],
         "logging_dir": str(logs_dir / job_name),
     }
@@ -267,12 +267,6 @@ def main():
         default="1950M",
         help="Memory per cpu that each worker will use.",
     )
-    parser.add_argument(
-        "--private",
-        action="store_true",
-        default=False,
-        help="Whether to create a private repository.",
-    )
 
     init_logging()
 

examples/rtc/eval_dataset.py

@@ -1,951 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Evaluate Real-Time Chunking (RTC) performance on dataset samples.
-
-This script takes two random samples from a dataset:
-- Uses actions from the first sample as previous chunk
-- Generates new actions for the second sample with and without RTC
-
-It compares action predictions with and without RTC on dataset samples,
-measuring consistency and ground truth alignment.
-
-Usage:
-    # Basic usage with smolvla policy
-    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --dataset.repo_id=helper2424/check_rtc \
-        --rtc.execution_horizon=8 \
-        --device=mps \
-        --rtc.max_guidance_weight=10.0 \
-        --rtc.prefix_attention_schedule=EXP \
-        --seed=10
-
-    # Basic usage with pi0.5 policy
-    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=lerobot/pi05_libero_finetuned \
-        --dataset.repo_id=HuggingFaceVLA/libero \
-        --rtc.execution_horizon=10 \
-        --device=mps
-        --seed=10
-
-    # Basic usage with pi0.5 policy with cuda device
-    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=lerobot/pi05_libero_finetuned \
-        --dataset.repo_id=HuggingFaceVLA/libero \
-        --rtc.execution_horizon=8 \
-        --device=cuda
-
-    # Basic usage with pi0 policy with cuda device
-    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=lerobot/pi0_libero_finetuned \
-        --dataset.repo_id=HuggingFaceVLA/libero \
-        --rtc.execution_horizon=8 \
-        --device=cuda
-
-    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=lipsop/reuben_pi0 \
-        --dataset.repo_id=ReubenLim/so101_cube_in_cup \
-        --rtc.execution_horizon=8 \
-        --device=cuda
-
-    # With torch.compile for faster inference (PyTorch 2.0+)
-    # Note: CUDA graphs disabled by default due to in-place ops in denoising loop
-    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --dataset.repo_id=helper2424/check_rtc \
-        --rtc.execution_horizon=8 \
-        --device=mps \
-        --use_torch_compile=true \
-        --torch_compile_mode=max-autotune
-
-    # With torch.compile on CUDA (CUDA graphs disabled by default)
-    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --dataset.repo_id=helper2424/check_rtc \
-        --rtc.execution_horizon=8 \
-        --device=cuda \
-        --use_torch_compile=true \
-        --torch_compile_mode=reduce-overhead
-
-    # Enable CUDA graphs (advanced - may cause tensor aliasing errors)
-    uv run python examples/rtc/eval_dataset.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --dataset.repo_id=helper2424/check_rtc \
-        --use_torch_compile=true \
-        --torch_compile_backend=inductor \
-        --torch_compile_mode=max-autotune \
-        --torch_compile_disable_cudagraphs=false
-"""
-
-import gc
-import logging
-import os
-import random
-from dataclasses import dataclass, field
-
-import numpy as np
-import torch
-
-try:
-    import matplotlib.pyplot as plt
-
-    MATPLOTLIB_AVAILABLE = True
-except ImportError:
-    MATPLOTLIB_AVAILABLE = False
-    plt = None
-
-from lerobot.configs import parser
-from lerobot.configs.default import DatasetConfig
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import RTCAttentionSchedule
-from lerobot.datasets.factory import resolve_delta_timestamps
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.policies.factory import get_policy_class, make_pre_post_processors
-from lerobot.policies.rtc.configuration_rtc import RTCConfig
-from lerobot.policies.rtc.debug_visualizer import RTCDebugVisualizer
-from lerobot.utils.hub import HubMixin
-from lerobot.utils.utils import init_logging
-
-
-def set_seed(seed: int):
-    """Set random seed for reproducibility."""
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-    if torch.backends.mps.is_available():
-        torch.mps.manual_seed(seed)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-
-
-def _check_matplotlib_available():
-    """Check if matplotlib is available, raise helpful error if not."""
-    if not MATPLOTLIB_AVAILABLE:
-        raise ImportError(
-            "matplotlib is required for RTC debug visualizations. "
-            "Please install it by running:\n"
-            "  uv pip install matplotlib"
-        )
-
-
-@dataclass
-class RTCEvalConfig(HubMixin):
-    """Configuration for RTC evaluation."""
-
-    # Policy configuration
-    policy: PreTrainedConfig | None = None
-
-    # Dataset configuration
-    dataset: DatasetConfig = field(default_factory=DatasetConfig)
-
-    # RTC configuration
-    rtc: RTCConfig = field(
-        default_factory=lambda: RTCConfig(
-            enabled=True,
-            execution_horizon=20,
-            max_guidance_weight=10.0,
-            prefix_attention_schedule=RTCAttentionSchedule.EXP,
-            debug=True,
-            debug_maxlen=1000,
-        )
-    )
-
-    # Device configuration
-    device: str | None = field(
-        default=None,
-        metadata={"help": "Device to run on (cuda, cpu, mps, auto)"},
-    )
-
-    # Output configuration
-    output_dir: str = field(
-        default="rtc_debug_output",
-        metadata={"help": "Directory to save debug visualizations"},
-    )
-
-    # Seed configuration
-    seed: int = field(
-        default=42,
-        metadata={"help": "Random seed for reproducibility"},
-    )
-
-    inference_delay: int = field(
-        default=4,
-        metadata={"help": "Inference delay for RTC"},
-    )
-
-    # Torch compile configuration
-    use_torch_compile: bool = field(
-        default=False,
-        metadata={"help": "Use torch.compile for faster inference (PyTorch 2.0+)"},
-    )
-
-    torch_compile_backend: str = field(
-        default="inductor",
-        metadata={"help": "Backend for torch.compile (inductor, aot_eager, cudagraphs)"},
-    )
-
-    torch_compile_mode: str = field(
-        default="default",
-        metadata={"help": "Compilation mode (default, reduce-overhead, max-autotune)"},
-    )
-
-    torch_compile_disable_cudagraphs: bool = field(
-        default=True,
-        metadata={
-            "help": "Disable CUDA graphs in torch.compile. Required due to in-place tensor "
-            "operations in denoising loop (x_t += dt * v_t) which cause tensor aliasing issues."
-        },
-    )
-
-    def __post_init__(self):
-        # Parse policy path
-        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
-        else:
-            raise ValueError("Policy path is required (--policy.path)")
-
-        # Auto-detect device if not specified
-        if self.device is None or self.device == "auto":
-            if torch.cuda.is_available():
-                self.device = "cuda"
-            elif torch.backends.mps.is_available():
-                self.device = "mps"
-            else:
-                self.device = "cpu"
-            logging.info(f"Auto-detected device: {self.device}")
-
-    @classmethod
-    def __get_path_fields__(cls) -> list[str]:
-        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
-        return ["policy"]
-
-
-class RTCEvaluator:
-    """Evaluator for RTC on dataset samples."""
-
-    def __init__(self, cfg: RTCEvalConfig):
-        self.cfg = cfg
-        self.device = cfg.device
-
-        # Load dataset with proper delta_timestamps based on policy configuration
-        # Calculate delta_timestamps using the same logic as make_dataset factory
-        logging.info(f"Loading dataset: {cfg.dataset.repo_id}")
-
-        # Get dataset metadata to extract FPS
-        ds_meta = LeRobotDatasetMetadata(cfg.dataset.repo_id)
-
-        # Calculate delta_timestamps from policy's delta_indices
-        delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
-
-        # Create dataset with calculated delta_timestamps
-        self.dataset = LeRobotDataset(
-            cfg.dataset.repo_id,
-            delta_timestamps=delta_timestamps,
-        )
-        logging.info(f"Dataset loaded: {len(self.dataset)} samples, {self.dataset.num_episodes} episodes")
-
-        # Create preprocessor/postprocessor
-        self.preprocessor, self.postprocessor = make_pre_post_processors(
-            policy_cfg=cfg.policy,
-            pretrained_path=cfg.policy.pretrained_path,
-            preprocessor_overrides={
-                "device_processor": {"device": self.device},
-            },
-        )
-
-        logging.info("=" * 80)
-        logging.info("Ready to run evaluation with sequential policy loading:")
-        logging.info("  1. policy_prev_chunk - Generate reference chunk, then destroy")
-        logging.info("  2. policy_no_rtc - Generate without RTC, then destroy")
-        logging.info("  3. policy_rtc - Generate with RTC, then destroy")
-        logging.info("  Note: Only one policy in memory at a time for efficient memory usage")
-        logging.info("=" * 80)
-
-    def _init_policy(self, name: str, rtc_enabled: bool, rtc_debug: bool):
-        """Initialize a single policy instance with specified RTC configuration.
-
-        Args:
-            name: Name identifier for logging purposes
-            rtc_enabled: Whether to enable RTC for this policy
-            rtc_debug: Whether to enable debug tracking for this policy
-
-        Returns:
-            Configured policy instance with optional torch.compile applied
-        """
-        logging.info(f"Initializing {name}...")
-
-        # Load policy from pretrained
-        policy_class = get_policy_class(self.cfg.policy.type)
-
-        config = PreTrainedConfig.from_pretrained(self.cfg.policy.pretrained_path)
-
-        if self.cfg.policy.type == "pi05" or self.cfg.policy.type == "pi0":
-            config.compile_model = self.cfg.use_torch_compile
-
-        policy = policy_class.from_pretrained(self.cfg.policy.pretrained_path, config=config)
-        policy = policy.to(self.device)
-        policy.eval()
-
-        # Configure RTC
-        rtc_config = RTCConfig(
-            enabled=rtc_enabled,
-            execution_horizon=self.cfg.rtc.execution_horizon,
-            max_guidance_weight=self.cfg.rtc.max_guidance_weight,
-            prefix_attention_schedule=self.cfg.rtc.prefix_attention_schedule,
-            debug=rtc_debug,
-            debug_maxlen=self.cfg.rtc.debug_maxlen,
-        )
-        policy.config.rtc_config = rtc_config
-        policy.init_rtc_processor()
-
-        logging.info(f"  RTC enabled: {rtc_enabled}")
-        logging.info(f"  RTC debug: {rtc_debug}")
-        logging.info(f"  Policy config: {config}")
-
-        # Apply torch.compile to predict_action_chunk method if enabled
-        if self.cfg.use_torch_compile:
-            policy = self._apply_torch_compile(policy, name)
-
-        logging.info(f"✓ {name} initialized successfully")
-        return policy
-
-    def _apply_torch_compile(self, policy, policy_name: str):
-        """Apply torch.compile to the policy's predict_action_chunk method.
-
-        Args:
-            policy: Policy instance to compile
-            policy_name: Name for logging purposes
-
-        Returns:
-            Policy with compiled predict_action_chunk method
-        """
-
-        # PI models handle their own compilation
-        if policy.type == "pi05" or policy.type == "pi0":
-            return policy
-
-        try:
-            # Check if torch.compile is available (PyTorch 2.0+)
-            if not hasattr(torch, "compile"):
-                logging.warning(
-                    f"  [{policy_name}] torch.compile is not available. Requires PyTorch 2.0+. "
-                    f"Current version: {torch.__version__}. Skipping compilation."
-                )
-                return policy
-
-            logging.info(f"  [{policy_name}] Applying torch.compile to predict_action_chunk...")
-            logging.info(f"    Backend: {self.cfg.torch_compile_backend}")
-            logging.info(f"    Mode: {self.cfg.torch_compile_mode}")
-            logging.info(f"    Disable CUDA graphs: {self.cfg.torch_compile_disable_cudagraphs}")
-            logging.info("    Note: Debug tracker excluded from compilation via @torch._dynamo.disable")
-
-            # Compile the predict_action_chunk method
-            # - Debug tracker is excluded from compilation via @torch._dynamo.disable
-            # - CUDA graphs disabled to prevent tensor aliasing from in-place ops (x_t += dt * v_t)
-            compile_kwargs = {
-                "backend": self.cfg.torch_compile_backend,
-                "mode": self.cfg.torch_compile_mode,
-            }
-
-            # Disable CUDA graphs if requested (prevents tensor aliasing issues)
-            if self.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
-            logging.info(f"  ✓ [{policy_name}] Successfully compiled predict_action_chunk")
-
-        except Exception as e:
-            logging.error(f"  [{policy_name}] Failed to apply torch.compile: {e}")
-            logging.warning(f"  [{policy_name}] Continuing without torch.compile")
-
-        return policy
-
-    def _destroy_policy(self, policy, policy_name: str):
-        """Explicitly destroy a policy and free all associated memory.
-
-        This method performs aggressive cleanup to ensure maximum memory is freed,
-        which is critical for large models (e.g., VLAs with billions of parameters).
-
-        Args:
-            policy: Policy instance to destroy
-            policy_name: Name for logging purposes
-        """
-        logging.info(f"  Destroying {policy_name} and freeing memory...")
-
-        try:
-            # Step 1: Move policy to CPU to free GPU/MPS memory
-            policy.cpu()
-
-            # Step 2: Delete the policy object
-            del policy
-
-            # Step 3: Force garbage collection to reclaim memory immediately
-            gc.collect()
-
-            # Step 4: Clear device-specific caches
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-                torch.cuda.synchronize()  # Ensure all operations complete
-
-            if torch.backends.mps.is_available():
-                torch.mps.empty_cache()
-
-            logging.info(f"  ✓ {policy_name} destroyed and memory freed")
-
-        except Exception as e:
-            logging.warning(f"  Warning: Error during {policy_name} cleanup: {e}")
-
-    def run_evaluation(self):
-        """Run evaluation on two random dataset samples using three separate policies.
-
-        Note: Policies are deinitalized after each step to free memory. Large models
-        (e.g., VLA models with billions of parameters) cannot fit three instances in
-        memory simultaneously. By deleting and garbage collecting after each step,
-        we ensure only one policy is loaded at a time.
-        """
-        # Create output directory
-        os.makedirs(self.cfg.output_dir, exist_ok=True)
-        logging.info(f"Output directory: {self.cfg.output_dir}")
-
-        logging.info("=" * 80)
-        logging.info("Starting RTC evaluation")
-        logging.info(f"Inference delay: {self.cfg.inference_delay}")
-        logging.info("=" * 80)
-
-        # Load two random samples from dataset
-        data_loader = torch.utils.data.DataLoader(self.dataset, batch_size=1, shuffle=True)
-        loader_iter = iter(data_loader)
-        first_sample = next(loader_iter)
-        second_sample = next(loader_iter)
-
-        preprocessed_first_sample = self.preprocessor(first_sample)
-        preprocessed_second_sample = self.preprocessor(second_sample)
-
-        # ============================================================================
-        # Step 1: Generate previous chunk using policy_prev_chunk
-        # ============================================================================
-        # This policy is only used to generate the reference chunk and then freed
-        logging.info("=" * 80)
-        logging.info("Step 1: Generating previous chunk with policy_prev_chunk")
-        logging.info("=" * 80)
-
-        # Initialize policy 1
-        policy_prev_chunk_policy = self._init_policy(
-            name="policy_prev_chunk",
-            rtc_enabled=False,
-            rtc_debug=False,
-        )
-        with torch.no_grad():
-            prev_chunk_left_over = policy_prev_chunk_policy.predict_action_chunk(
-                preprocessed_first_sample,
-            )[:, :25, :].squeeze(0)
-        logging.info(f"  Generated prev_chunk shape: {prev_chunk_left_over.shape}")
-
-        # Destroy policy_prev_chunk to free memory for large models
-        self._destroy_policy(policy_prev_chunk_policy, "policy_prev_chunk")
-
-        # ============================================================================
-        # Step 2: Generate actions WITHOUT RTC using policy_no_rtc
-        # ============================================================================
-        logging.info("=" * 80)
-        logging.info("Step 2: Generating actions WITHOUT RTC with policy_no_rtc")
-        logging.info("=" * 80)
-
-        set_seed(self.cfg.seed)
-
-        # Initialize policy 2
-        policy_no_rtc_policy = self._init_policy(
-            name="policy_no_rtc",
-            rtc_enabled=False,
-            rtc_debug=True,
-        )
-
-        # Sample noise (use same noise for both RTC and non-RTC for fair comparison)
-        noise_size = (1, policy_no_rtc_policy.config.chunk_size, policy_no_rtc_policy.config.max_action_dim)
-        noise = policy_no_rtc_policy.model.sample_noise(noise_size, self.device)
-        noise_clone = noise.clone()
-        policy_no_rtc_policy.rtc_processor.reset_tracker()
-        with torch.no_grad():
-            no_rtc_actions = policy_no_rtc_policy.predict_action_chunk(
-                preprocessed_second_sample,
-                noise=noise,
-            )
-        no_rtc_tracked_steps = policy_no_rtc_policy.rtc_processor.tracker.get_all_steps()
-        logging.info(f"  Tracked {len(no_rtc_tracked_steps)} steps without RTC")
-        logging.info(f"  Generated no_rtc_actions shape: {no_rtc_actions.shape}")
-
-        # Destroy policy_no_rtc to free memory before loading policy_rtc
-        self._destroy_policy(policy_no_rtc_policy, "policy_no_rtc")
-
-        # ============================================================================
-        # Step 3: Generate actions WITH RTC using policy_rtc
-        # ============================================================================
-        logging.info("=" * 80)
-        logging.info("Step 3: Generating actions WITH RTC with policy_rtc")
-        logging.info("=" * 80)
-
-        set_seed(self.cfg.seed)
-
-        # Initialize policy 3
-        policy_rtc_policy = self._init_policy(
-            name="policy_rtc",
-            rtc_enabled=True,
-            rtc_debug=True,
-        )
-        policy_rtc_policy.rtc_processor.reset_tracker()
-        with torch.no_grad():
-            rtc_actions = policy_rtc_policy.predict_action_chunk(
-                preprocessed_second_sample,
-                noise=noise_clone,
-                inference_delay=self.cfg.inference_delay,
-                prev_chunk_left_over=prev_chunk_left_over,
-                execution_horizon=self.cfg.rtc.execution_horizon,
-            )
-        rtc_tracked_steps = policy_rtc_policy.rtc_processor.get_all_debug_steps()
-        logging.info(f"  Tracked {len(rtc_tracked_steps)} steps with RTC")
-        logging.info(f"  Generated rtc_actions shape: {rtc_actions.shape}")
-
-        # Save num_steps before destroying policy (needed for plotting)
-        try:
-            num_steps = policy_rtc_policy.config.num_steps
-        except Exception as e:
-            logging.error(f"  Error getting num_steps: {e}")
-            num_steps = policy_rtc_policy.config.num_inference_steps
-            logging.warning(f"  Using num_inference_steps: {num_steps} instead of num_steps")
-
-        # Destroy policy_rtc after final use
-        self._destroy_policy(policy_rtc_policy, "policy_rtc")
-
-        # Plot and save results
-        logging.info("=" * 80)
-        logging.info("Plotting results...")
-        self.plot_tracked_data(rtc_tracked_steps, no_rtc_tracked_steps, prev_chunk_left_over, num_steps)
-
-        # Plot final actions comparison
-        logging.info("=" * 80)
-        logging.info("Plotting final actions comparison...")
-        self.plot_final_actions_comparison(rtc_actions, no_rtc_actions, prev_chunk_left_over)
-
-        logging.info("=" * 80)
-        logging.info("Evaluation completed successfully")
-
-    def plot_final_actions_comparison(self, rtc_actions, no_rtc_actions, prev_chunk_left_over):
-        """Plot final action predictions comparison on a single chart.
-
-        Args:
-            rtc_actions: Final actions from RTC policy
-            no_rtc_actions: Final actions from non-RTC policy
-            prev_chunk_left_over: Previous chunk used as ground truth
-        """
-        _check_matplotlib_available()
-
-        # Remove batch dimension if present
-        rtc_actions_plot = rtc_actions.squeeze(0).cpu() if len(rtc_actions.shape) == 3 else rtc_actions.cpu()
-        no_rtc_actions_plot = (
-            no_rtc_actions.squeeze(0).cpu() if len(no_rtc_actions.shape) == 3 else no_rtc_actions.cpu()
-        )
-        prev_chunk_plot = prev_chunk_left_over.cpu()
-
-        # Create figure with 6 subplots (one per action dimension)
-        fig, axes = plt.subplots(6, 1, figsize=(16, 12))
-        fig.suptitle("Final Action Predictions Comparison (Raw)", fontsize=16)
-
-        # Plot each action dimension
-        for dim_idx, ax in enumerate(axes):
-            # Plot previous chunk (ground truth) in red
-            RTCDebugVisualizer.plot_waypoints(
-                [ax],
-                prev_chunk_plot[:, dim_idx : dim_idx + 1],
-                start_from=0,
-                color="red",
-                label="Previous Chunk (Ground Truth)",
-                linewidth=2.5,
-                alpha=0.8,
-            )
-
-            # Plot no-RTC actions in blue
-            RTCDebugVisualizer.plot_waypoints(
-                [ax],
-                no_rtc_actions_plot[:, dim_idx : dim_idx + 1],
-                start_from=0,
-                color="blue",
-                label="No RTC",
-                linewidth=2,
-                alpha=0.7,
-            )
-
-            # Plot RTC actions in green
-            RTCDebugVisualizer.plot_waypoints(
-                [ax],
-                rtc_actions_plot[:, dim_idx : dim_idx + 1],
-                start_from=0,
-                color="green",
-                label="RTC",
-                linewidth=2,
-                alpha=0.7,
-            )
-
-            # Add vertical lines for inference delay and execution horizon
-            inference_delay = self.cfg.inference_delay
-            execution_horizon = self.cfg.rtc.execution_horizon
-
-            if inference_delay > 0:
-                ax.axvline(
-                    x=inference_delay - 1,
-                    color="orange",
-                    linestyle="--",
-                    alpha=0.5,
-                    label=f"Inference Delay ({inference_delay})",
-                )
-
-            if execution_horizon > 0:
-                ax.axvline(
-                    x=execution_horizon,
-                    color="purple",
-                    linestyle="--",
-                    alpha=0.5,
-                    label=f"Execution Horizon ({execution_horizon})",
-                )
-
-            ax.set_ylabel(f"Dim {dim_idx}", fontsize=10)
-            ax.grid(True, alpha=0.3)
-
-            # Set x-axis ticks to show all integer values
-            max_len = max(rtc_actions_plot.shape[0], no_rtc_actions_plot.shape[0], prev_chunk_plot.shape[0])
-            ax.set_xticks(range(0, max_len, max(1, max_len // 20)))  # Show ~20 ticks
-            ax.set_xlim(-0.5, max_len - 0.5)
-
-        axes[-1].set_xlabel("Step", fontsize=10)
-
-        # Collect legend handles and labels from first subplot
-        handles, labels = axes[0].get_legend_handles_labels()
-        # Remove duplicates while preserving order
-        seen = set()
-        unique_handles = []
-        unique_labels = []
-        for handle, label in zip(handles, labels, strict=True):
-            if label not in seen:
-                seen.add(label)
-                unique_handles.append(handle)
-                unique_labels.append(label)
-
-        # Add legend outside the plot area (to the right)
-        fig.legend(
-            unique_handles,
-            unique_labels,
-            loc="center right",
-            fontsize=9,
-            bbox_to_anchor=(1.0, 0.5),
-            framealpha=0.9,
-        )
-
-        # Save figure
-        output_path = os.path.join(self.cfg.output_dir, "final_actions_comparison.png")
-        fig.tight_layout(rect=[0, 0, 0.85, 1])  # Leave space for legend on right
-        fig.savefig(output_path, dpi=150, bbox_inches="tight")
-        logging.info(f"Saved final actions comparison to {output_path}")
-        plt.close(fig)
-
-    def plot_tracked_data(self, rtc_tracked_steps, no_rtc_tracked_steps, prev_chunk_left_over, num_steps):
-        _check_matplotlib_available()
-
-        # Create side-by-side figures for denoising visualization
-        fig_xt, axs_xt = self._create_figure("x_t Denoising: No RTC (left) vs RTC (right)")
-        fig_vt, axs_vt = self._create_figure("v_t Denoising: No RTC (left) vs RTC (right)")
-        fig_corr, axs_corr = self._create_figure("Correction: No RTC (left) vs RTC (right)")
-        fig_x1t, axs_x1t = self._create_figure(
-            "x1_t Predicted State & Error: No RTC (left - empty) vs RTC (right)"
-        )
-        self._plot_denoising_steps_from_tracker(
-            rtc_tracked_steps,
-            axs_xt[:, 1],  # Right column for x_t
-            axs_vt[:, 1],  # Right column for v_t
-            axs_corr[:, 1],  # Right column for correction
-            axs_x1t[:, 1],  # Right column for x1_t
-            num_steps,
-            add_labels=True,  # Add labels for RTC (right column)
-        )
-
-        self._plot_denoising_steps_from_tracker(
-            no_rtc_tracked_steps,
-            axs_xt[:, 0],  # Left column for x_t
-            axs_vt[:, 0],  # Left column for v_t
-            axs_corr[:, 0],  # Left column for correction
-            axs_x1t[:, 0],  # Left column for x1_t
-            num_steps,
-            add_labels=False,  # No labels for No RTC (left column)
-        )
-
-        # Plot no-RTC x_t data on right chart as orange dashed line for comparison
-        self._plot_no_rtc_xt_reference(no_rtc_tracked_steps, axs_xt[:, 1], num_steps)
-
-        # Plot ground truth on x_t axes
-        RTCDebugVisualizer.plot_waypoints(
-            axs_xt[:, 1], prev_chunk_left_over, start_from=0, color="red", label="Ground truth"
-        )
-
-        # Plot ground truth on x1_t axes
-        RTCDebugVisualizer.plot_waypoints(
-            axs_x1t[:, 1], prev_chunk_left_over, start_from=0, color="red", label="Ground truth"
-        )
-
-        # Plot ground truth on x_t axes (no labels for left column)
-        RTCDebugVisualizer.plot_waypoints(
-            axs_xt[:, 0], prev_chunk_left_over, start_from=0, color="red", label=None
-        )
-
-        RTCDebugVisualizer.plot_waypoints(
-            axs_x1t[:, 0], prev_chunk_left_over, start_from=0, color="red", label=None
-        )
-
-        # Add legends outside the plot area for each figure
-        self._add_figure_legend(fig_xt, axs_xt)
-        self._add_figure_legend(fig_vt, axs_vt)
-        self._add_figure_legend(fig_corr, axs_corr)
-        self._add_figure_legend(fig_x1t, axs_x1t)
-
-        # Save denoising plots
-        self._save_figure(fig_xt, os.path.join(self.cfg.output_dir, "denoising_xt_comparison.png"))
-        self._save_figure(fig_vt, os.path.join(self.cfg.output_dir, "denoising_vt_comparison.png"))
-        self._save_figure(fig_corr, os.path.join(self.cfg.output_dir, "denoising_correction_comparison.png"))
-        self._save_figure(fig_x1t, os.path.join(self.cfg.output_dir, "denoising_x1t_comparison.png"))
-
-    def _create_figure(self, title):
-        fig, axs = plt.subplots(6, 2, figsize=(24, 12))
-        fig.suptitle(title, fontsize=16)
-
-        for ax in axs[:, 0]:
-            ax.set_title("No RTC (N/A)" if ax == axs[0, 0] else "", fontsize=12)
-        for ax in axs[:, 1]:
-            ax.set_title("RTC" if ax == axs[0, 1] else "", fontsize=12)
-
-        return fig, axs
-
-    def _add_figure_legend(self, fig, axs):
-        """Add a legend outside the plot area on the right side.
-
-        Args:
-            fig: Matplotlib figure to add legend to
-            axs: Array of axes to collect legend handles from
-        """
-        # Collect all handles and labels from the first row of axes (right column)
-        handles, labels = axs[0, 1].get_legend_handles_labels()
-
-        # Remove duplicates while preserving order
-        seen = set()
-        unique_handles = []
-        unique_labels = []
-        for handle, label in zip(handles, labels, strict=True):
-            if label not in seen:
-                seen.add(label)
-                unique_handles.append(handle)
-                unique_labels.append(label)
-
-        # Add legend outside the plot area (to the right, close to charts)
-        if unique_handles:
-            fig.legend(
-                unique_handles,
-                unique_labels,
-                loc="center left",
-                fontsize=8,
-                bbox_to_anchor=(0.87, 0.5),
-                framealpha=0.9,
-                ncol=1,
-            )
-
-    def _save_figure(self, fig, path):
-        fig.tight_layout(rect=[0, 0, 0.85, 1])  # Leave space for legend/colorbar on right
-        fig.savefig(path, dpi=150, bbox_inches="tight")
-        logging.info(f"Saved figure to {path}")
-        plt.close(fig)
-
-    def _plot_denoising_steps_from_tracker(
-        self, tracked_steps, xt_axs, vt_axs, corr_axs, x1t_axs, num_steps, add_labels=True
-    ):
-        """Plot denoising steps from tracker data.
-
-        Args:
-            tracked_steps: List of DebugStep objects containing debug steps
-            xt_axs: Matplotlib axes for x_t plots (array of 6 axes)
-            vt_axs: Matplotlib axes for v_t plots (array of 6 axes)
-            corr_axs: Matplotlib axes for correction plots (array of 6 axes)
-            x1t_axs: Matplotlib axes for x1_t plots (array of 6 axes)
-            num_steps: Total number of denoising steps for colormap
-            add_labels: Whether to add legend labels for the plots
-        """
-
-        logging.info("=" * 80)
-        logging.info(f"Plotting {len(tracked_steps)} steps")
-
-        debug_steps = tracked_steps
-        if not debug_steps:
-            return
-
-        # Define colors for different denoise steps (using a colormap)
-        colors = plt.cm.viridis(np.linspace(0, 1, num_steps))
-
-        for step_idx, debug_step in enumerate(debug_steps):
-            color = colors[step_idx % len(colors)]
-            label = f"Step {step_idx}" if add_labels else None
-
-            # Plot x_t
-            if debug_step.x_t is not None:
-                RTCDebugVisualizer.plot_waypoints(
-                    xt_axs, debug_step.x_t, start_from=0, color=color, label=label
-                )
-
-            # Plot v_t
-            if debug_step.v_t is not None:
-                RTCDebugVisualizer.plot_waypoints(
-                    vt_axs, debug_step.v_t, start_from=0, color=color, label=label
-                )
-
-            # Plot correction on separate axes
-            if debug_step.correction is not None:
-                RTCDebugVisualizer.plot_waypoints(
-                    corr_axs,
-                    debug_step.correction,
-                    start_from=0,
-                    color=color,
-                    label=label,
-                )
-
-            # Plot x1_t (predicted state)
-            if x1t_axs is not None and debug_step.x1_t is not None:
-                x1t_label = f"x1_t Step {step_idx}" if add_labels else None
-                RTCDebugVisualizer.plot_waypoints(
-                    x1t_axs,
-                    debug_step.x1_t,
-                    start_from=0,
-                    color=color,
-                    label=x1t_label,
-                )
-
-            # Plot error in orange dashed
-            if x1t_axs is not None and debug_step.err is not None:
-                error_chunk = (
-                    debug_step.err[0].cpu().numpy()
-                    if len(debug_step.err.shape) == 3
-                    else debug_step.err.cpu().numpy()
-                )
-
-                num_dims = min(error_chunk.shape[-1], 6)
-                error_label = f"error Step {step_idx}" if add_labels else None
-                for j in range(num_dims):
-                    x1t_axs[j].plot(
-                        np.arange(0, error_chunk.shape[0]),
-                        error_chunk[:, j],
-                        color="orange",
-                        linestyle="--",
-                        alpha=0.7,
-                        label=error_label,
-                    )
-
-        # Recalculate axis limits after plotting to ensure proper scaling
-        self._rescale_axes(xt_axs)
-        self._rescale_axes(vt_axs)
-        self._rescale_axes(corr_axs)
-        self._rescale_axes(x1t_axs)
-
-    def _plot_no_rtc_xt_reference(self, no_rtc_tracked_steps, xt_axs, num_steps):
-        """Plot final no-RTC x_t data as orange dashed line on the RTC chart for comparison.
-
-        Args:
-            no_rtc_tracked_steps: List of DebugStep objects containing no-RTC debug steps
-            xt_axs: Matplotlib axes for x_t plots (array of 6 axes, right column)
-            num_steps: Total number of denoising steps for colormap
-        """
-        debug_steps = no_rtc_tracked_steps
-        if not debug_steps:
-            return
-
-        # Plot only the final x_t step as orange dashed line
-        final_step = debug_steps[-1]
-        logging.info("Plotting final no-RTC x_t step as orange dashed reference")
-
-        if final_step.x_t is not None:
-            x_t_chunk = (
-                final_step.x_t[0].cpu().numpy()
-                if len(final_step.x_t.shape) == 3
-                else final_step.x_t.cpu().numpy()
-            )
-
-            num_dims = min(x_t_chunk.shape[-1], 6)
-            for j in range(num_dims):
-                xt_axs[j].plot(
-                    np.arange(0, x_t_chunk.shape[0]),
-                    x_t_chunk[:, j],
-                    color="orange",
-                    linestyle="--",
-                    alpha=0.7,
-                    linewidth=2,
-                    label="No RTC (final)" if j == 0 else "",
-                )
-
-    def _rescale_axes(self, axes):
-        """Rescale axes to show all data with proper margins.
-
-        Args:
-            axes: Array of matplotlib axes to rescale
-        """
-        for ax in axes:
-            ax.relim()
-            ax.autoscale_view()
-
-            # Add 10% margin to y-axis for better visualization
-            ylim = ax.get_ylim()
-            y_range = ylim[1] - ylim[0]
-            if y_range > 0:  # Avoid division by zero
-                margin = y_range * 0.1
-                ax.set_ylim(ylim[0] - margin, ylim[1] + margin)
-
-            # Set x-axis ticks to show all integer values
-            xlim = ax.get_xlim()
-            max_len = int(xlim[1]) + 1
-            if max_len > 0:
-                ax.set_xticks(range(0, max_len, max(1, max_len // 20)))  # Show ~20 ticks
-                ax.set_xlim(-0.5, max_len - 0.5)
-
-
-@parser.wrap()
-def main(cfg: RTCEvalConfig):
-    """Main entry point for RTC evaluation."""
-    # Set random seed for reproducibility
-    set_seed(cfg.seed)
-
-    init_logging()
-
-    logging.info("=" * 80)
-    logging.info("RTC Dataset Evaluation")
-    logging.info(f"Config: {cfg}")
-    logging.info("=" * 80)
-
-    evaluator = RTCEvaluator(cfg)
-    evaluator.run_evaluation()
-
-
-if __name__ == "__main__":
-    main()

examples/rtc/eval_with_real_robot.py

@@ -1,549 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Demo script showing how to use Real-Time Chunking (RTC) with action chunking policies on real robots.
-
-This script demonstrates:
-1. Creating a robot and policy (SmolVLA, Pi0, etc.) with RTC
-2. Consuming actions from the policy while the robot executes
-3. Periodically requesting new action chunks in the background using threads
-4. Managing action buffers and timing for real-time operation
-
-For simulation environments, see eval_with_simulation.py
-
-Usage:
-    # Run RTC with Real robot with RTC
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --policy.device=mps \
-        --rtc.enabled=true \
-        --rtc.execution_horizon=20 \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-
-    # Run RTC with Real robot without RTC
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --policy.device=mps \
-        --rtc.enabled=false \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-
-    # Run RTC with Real robot with pi0.5 policy
-    uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=helper2424/pi05_check_rtc \
-        --policy.device=mps \
-        --rtc.enabled=true \
-        --rtc.execution_horizon=20 \
-        --robot.type=so100_follower \
-        --robot.port=/dev/tty.usbmodem58FA0834591 \
-        --robot.id=so100_follower \
-        --robot.cameras="{ gripper: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}}" \
-        --task="Move green small object into the purple platform" \
-        --duration=120
-"""
-
-import logging
-import math
-import sys
-import time
-import traceback
-from dataclasses import dataclass, field
-from threading import Event, Lock, Thread
-
-import torch
-from torch import Tensor
-
-from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
-from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.configs import parser
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import RTCAttentionSchedule
-from lerobot.datasets.utils import build_dataset_frame, 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.factory import (
-    make_default_robot_action_processor,
-    make_default_robot_observation_processor,
-)
-from lerobot.rl.process import ProcessSignalHandler
-from lerobot.robots import (  # noqa: F401
-    Robot,
-    RobotConfig,
-    koch_follower,
-    so100_follower,
-    so101_follower,
-)
-from lerobot.robots.utils import make_robot_from_config
-from lerobot.utils.constants import OBS_IMAGES
-from lerobot.utils.hub import HubMixin
-from lerobot.utils.utils import init_logging
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class RobotWrapper:
-    def __init__(self, robot: Robot):
-        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: Tensor):
-        with self.lock:
-            self.robot.send_action(action)
-
-    def observation_features(self) -> list[str]:
-        with self.lock:
-            return self.robot.observation_features
-
-    def action_features(self) -> list[str]:
-        with self.lock:
-            return self.robot.action_features
-
-
-@dataclass
-class RTCDemoConfig(HubMixin):
-    """Configuration for RTC demo with action chunking policies and real robots."""
-
-    # Policy configuration
-    policy: PreTrainedConfig | None = None
-
-    # Robot configuration
-    robot: RobotConfig | None = None
-
-    # RTC configuration
-    rtc: RTCConfig = field(
-        default_factory=lambda: RTCConfig(
-            execution_horizon=10,
-            max_guidance_weight=1.0,
-            prefix_attention_schedule=RTCAttentionSchedule.EXP,
-        )
-    )
-
-    # Demo parameters
-    duration: float = 30.0  # Duration to run the demo (seconds)
-    fps: float = 10.0  # Action execution frequency (Hz)
-
-    # Compute device
-    device: str | None = None  # Device to run on (cuda, cpu, auto)
-
-    # Get new actions horizon. The amount of executed steps after which will be requested new actions.
-    # It should be higher than inference delay + execution horizon.
-    action_queue_size_to_get_new_actions: int = 30
-
-    # Task to execute
-    task: str = field(default="", metadata={"help": "Task to execute"})
-
-    # Torch compile configuration
-    use_torch_compile: bool = field(
-        default=False,
-        metadata={"help": "Use torch.compile for faster inference (PyTorch 2.0+)"},
-    )
-
-    torch_compile_backend: str = field(
-        default="inductor",
-        metadata={"help": "Backend for torch.compile (inductor, aot_eager, cudagraphs)"},
-    )
-
-    torch_compile_mode: str = field(
-        default="default",
-        metadata={"help": "Compilation mode (default, reduce-overhead, max-autotune)"},
-    )
-
-    torch_compile_disable_cudagraphs: bool = field(
-        default=True,
-        metadata={
-            "help": "Disable CUDA graphs in torch.compile. Required due to in-place tensor "
-            "operations in denoising loop (x_t += dt * v_t) which cause tensor aliasing issues."
-        },
-    )
-
-    def __post_init__(self):
-        # HACK: We parse again the cli args here to get the pretrained path if there was one.
-        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
-        else:
-            raise ValueError("Policy path is required")
-
-        # Validate that robot configuration is provided
-        if self.robot is None:
-            raise ValueError("Robot configuration must be provided")
-
-    @classmethod
-    def __get_path_fields__(cls) -> list[str]:
-        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
-        return ["policy"]
-
-
-def is_image_key(k: str) -> bool:
-    return k.startswith(OBS_IMAGES)
-
-
-def get_actions(
-    policy,
-    robot: RobotWrapper,
-    robot_observation_processor,
-    action_queue: ActionQueue,
-    shutdown_event: Event,
-    cfg: RTCDemoConfig,
-):
-    """Thread function to request action chunks from the policy.
-
-    Args:
-        policy: The policy instance (SmolVLA, Pi0, etc.)
-        robot: The robot instance for getting observations
-        robot_observation_processor: Processor for raw robot observations
-        action_queue: Queue to put new action chunks
-        shutdown_event: Event to signal shutdown
-        cfg: Demo configuration
-    """
-    try:
-        logger.info("[GET_ACTIONS] Starting get actions thread")
-
-        latency_tracker = LatencyTracker()  # Track latency of action chunks
-        fps = cfg.fps
-        time_per_chunk = 1.0 / fps
-
-        dataset_features = hw_to_dataset_features(robot.observation_features(), "observation")
-        policy_device = policy.config.device
-
-        # Load preprocessor and postprocessor from pretrained files
-        # The stats are embedded in the processor .safetensors files
-        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,  # Will load from pretrained processor files
-            preprocessor_overrides={
-                "device_processor": {"device": cfg.policy.device},
-            },
-        )
-
-        logger.info("[GET_ACTIONS] Preprocessor/postprocessor loaded successfully with embedded stats")
-
-        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 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)
-
-                obs = robot.get_observation()
-
-                # Apply robot observation processor
-                obs_processed = robot_observation_processor(obs)
-
-                obs_with_policy_features = build_dataset_frame(
-                    dataset_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]  # Task should be a list, not a string!
-                obs_with_policy_features["robot_type"] = (
-                    robot.robot.name if hasattr(robot.robot, "name") else ""
-                )
-
-                preproceseded_obs = preprocessor(obs_with_policy_features)
-
-                # Generate actions WITH RTC
-                actions = policy.predict_action_chunk(
-                    preproceseded_obs,
-                    inference_delay=inference_delay,
-                    prev_chunk_left_over=prev_actions,
-                )
-
-                # Store original actions (before postprocessing) for RTC
-                original_actions = actions.squeeze(0).clone()
-
-                postprocessed_actions = postprocessor(actions)
-
-                postprocessed_actions = postprocessed_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] cfg.action_queue_size_to_get_new_actions Too small, It should be higher than inference delay + execution horizon."
-                    )
-
-                action_queue.merge(
-                    original_actions, postprocessed_actions, new_delay, action_index_before_inference
-                )
-            else:
-                # Small sleep to prevent busy waiting
-                time.sleep(0.1)
-
-        logger.info("[GET_ACTIONS] get actions thread shutting down")
-    except Exception as e:
-        logger.error(f"[GET_ACTIONS] Fatal exception in get_actions thread: {e}")
-        logger.error(traceback.format_exc())
-        sys.exit(1)
-
-
-def actor_control(
-    robot: RobotWrapper,
-    robot_action_processor,
-    action_queue: ActionQueue,
-    shutdown_event: Event,
-    cfg: RTCDemoConfig,
-):
-    """Thread function to execute actions on the robot.
-
-    Args:
-        robot: The robot instance
-        action_queue: Queue to get actions from
-        shutdown_event: Event to signal shutdown
-        cfg: Demo configuration
-    """
-    try:
-        logger.info("[ACTOR] Starting actor thread")
-
-        action_count = 0
-        action_interval = 1.0 / cfg.fps
-
-        while not shutdown_event.is_set():
-            start_time = time.perf_counter()
-
-            # Try to get an action from the queue with timeout
-            action = action_queue.get()
-
-            if action is not None:
-                action = action.cpu()
-                action_dict = {key: action[i].item() for i, key in enumerate(robot.action_features())}
-                action_processed = robot_action_processor((action_dict, None))
-                robot.send_action(action_processed)
-
-                action_count += 1
-
-            dt_s = time.perf_counter() - start_time
-            time.sleep(max(0, (action_interval - dt_s) - 0.001))
-
-        logger.info(f"[ACTOR] Actor thread shutting down. Total actions executed: {action_count}")
-    except Exception as e:
-        logger.error(f"[ACTOR] Fatal exception in actor_control thread: {e}")
-        logger.error(traceback.format_exc())
-        sys.exit(1)
-
-
-def _apply_torch_compile(policy, cfg: RTCDemoConfig):
-    """Apply torch.compile to the policy's predict_action_chunk method.
-
-    Args:
-        policy: Policy instance to compile
-        cfg: Configuration containing torch compile settings
-
-    Returns:
-        Policy with compiled predict_action_chunk method
-    """
-
-    # PI models handle their own compilation
-    if policy.type == "pi05" or policy.type == "pi0":
-        return policy
-
-    try:
-        # Check if torch.compile is available (PyTorch 2.0+)
-        if not hasattr(torch, "compile"):
-            logger.warning(
-                f"torch.compile is not available. Requires PyTorch 2.0+. "
-                f"Current version: {torch.__version__}. Skipping compilation."
-            )
-            return policy
-
-        logger.info("Applying torch.compile to predict_action_chunk...")
-        logger.info(f"  Backend: {cfg.torch_compile_backend}")
-        logger.info(f"  Mode: {cfg.torch_compile_mode}")
-        logger.info(f"  Disable CUDA graphs: {cfg.torch_compile_disable_cudagraphs}")
-
-        # Compile the predict_action_chunk method
-        # - CUDA graphs disabled to prevent tensor aliasing from in-place ops (x_t += dt * v_t)
-        compile_kwargs = {
-            "backend": cfg.torch_compile_backend,
-            "mode": cfg.torch_compile_mode,
-        }
-
-        # Disable CUDA graphs if requested (prevents tensor aliasing issues)
-        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 demo_cli(cfg: RTCDemoConfig):
-    """Main entry point for RTC demo with draccus configuration."""
-
-    # Initialize logging
-    init_logging()
-
-    logger.info(f"Using device: {cfg.device}")
-
-    # Setup signal handler for graceful shutdown
-    signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
-    shutdown_event = signal_handler.shutdown_event
-
-    policy = None
-    robot = None
-    get_actions_thread = None
-    actor_thread = None
-
-    policy_class = get_policy_class(cfg.policy.type)
-
-    # Load config and set compile_model for pi0/pi05 models
-    config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
-
-    if cfg.policy.type == "pi05" or cfg.policy.type == "pi0":
-        config.compile_model = cfg.use_torch_compile
-
-    policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
-
-    # Turn on RTC
-    policy.config.rtc_config = cfg.rtc
-
-    # Init RTC processort, as by default if RTC disabled in the config
-    # The processor won't be created
-    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()
-
-    # Apply torch.compile to predict_action_chunk method if enabled
-    if cfg.use_torch_compile:
-        policy = _apply_torch_compile(policy, cfg)
-
-    # Create robot
-    logger.info(f"Initializing robot: {cfg.robot.type}")
-    robot = make_robot_from_config(cfg.robot)
-    robot.connect()
-    robot_wrapper = RobotWrapper(robot)
-
-    # Create robot observation processor
-    robot_observation_processor = make_default_robot_observation_processor()
-    robot_action_processor = make_default_robot_action_processor()
-
-    # Create action queue for communication between threads
-    action_queue = ActionQueue(cfg.rtc)
-
-    # Start chunk requester thread
-    get_actions_thread = Thread(
-        target=get_actions,
-        args=(policy, robot_wrapper, robot_observation_processor, action_queue, shutdown_event, cfg),
-        daemon=True,
-        name="GetActions",
-    )
-    get_actions_thread.start()
-    logger.info("Started get actions thread")
-
-    # Start action executor thread
-    actor_thread = Thread(
-        target=actor_control,
-        args=(robot_wrapper, robot_action_processor, action_queue, shutdown_event, cfg),
-        daemon=True,
-        name="Actor",
-    )
-    actor_thread.start()
-    logger.info("Started actor thread")
-
-    logger.info("Started stop by duration thread")
-
-    # Main thread monitors for duration or shutdown
-    logger.info(f"Running demo for {cfg.duration} seconds...")
-    start_time = time.time()
-
-    while not shutdown_event.is_set() and (time.time() - start_time) < cfg.duration:
-        time.sleep(10)
-
-        # Log queue status periodically
-        if int(time.time() - start_time) % 5 == 0:
-            logger.info(f"[MAIN] Action queue size: {action_queue.qsize()}")
-
-        if time.time() - start_time > cfg.duration:
-            break
-
-    logger.info("Demo duration reached or shutdown requested")
-
-    # Signal shutdown
-    shutdown_event.set()
-
-    # Wait for threads to finish
-    if get_actions_thread and get_actions_thread.is_alive():
-        logger.info("Waiting for chunk requester thread to finish...")
-        get_actions_thread.join()
-
-    if actor_thread and actor_thread.is_alive():
-        logger.info("Waiting for action executor thread to finish...")
-        actor_thread.join()
-
-    # Cleanup robot
-    if robot:
-        robot.disconnect()
-        logger.info("Robot disconnected")
-
-    logger.info("Cleanup completed")
-
-
-if __name__ == "__main__":
-    demo_cli()
-    logging.info("RTC demo finished")

examples/so100_to_so100_EE/evaluate.py

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

examples/so100_to_so100_EE/record.py

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

examples/so100_to_so100_EE/replay.py

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

examples/so100_to_so100_EE/teleoperate.py

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

examples/tutorial/act/act_training_example.py

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

examples/tutorial/act/act_using_example.py

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

examples/tutorial/async-inf/policy_server.py

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

examples/tutorial/async-inf/robot_client.py

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

examples/tutorial/diffusion/diffusion_training_example.py

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

examples/tutorial/diffusion/diffusion_using_example.py

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

examples/tutorial/pi0/using_pi0_example.py

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

examples/tutorial/rl/hilserl_example.py

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

examples/tutorial/rl/reward_classifier_example.py

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

examples/tutorial/smolvla/using_smolvla_example.py

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

examples/unitree_g1/dance.py

@@ -1,454 +0,0 @@
-#!/usr/bin/env python3
-"""
-WBT (Whole Body Tracking) Dance Policy for Unitree G1
-
-Uses ONNX model with motion data baked in.
-Pattern matches gr00t_locomotion.py - uses UnitreeG1 robot class.
-
-Usage:
-    python examples/unitree_g1/dance.py
-"""
-
-import argparse
-import json
-import logging
-import threading
-import time
-from xml.etree import ElementTree
-
-import numpy as np
-import onnx
-import onnxruntime as ort
-import pinocchio as pin
-
-from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
-from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# =============================================================================
-# CONFIGURATION
-# =============================================================================
-
-DANCE_ONNX_PATH = "examples/unitree_g1/fastsac_g1_29dof_dancing.onnx"
-CONTROL_DT = 0.02  # 50 Hz
-NUM_DOFS = 29
-
-# Default joint positions (holosoma training defaults)
-DEFAULT_DOF_POS = np.array([
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,   # Left leg (6)
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,   # Right leg (6)
-    0.0, 0.0, 0.0,                           # Waist (3)
-    0.2, 0.2, 0.0, 0.6, 0.0, 0.0, 0.0,      # Left arm (7)
-    0.2, -0.2, 0.0, 0.6, 0.0, 0.0, 0.0,     # Right arm (7)
-], dtype=np.float32)
-
-# Stiff hold KP/KD (for initialization)
-STIFF_KP = np.array([
-    150, 150, 200, 200, 40, 40,
-    150, 150, 200, 200, 40, 40,
-    200, 200, 100,
-    100, 100, 100, 100, 50, 50, 50,
-    100, 100, 100, 100, 50, 50, 50,
-], dtype=np.float32)
-
-STIFF_KD = np.array([
-    2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
-    2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
-    5.0, 5.0, 5.0,
-    2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
-    2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5,
-], dtype=np.float32)
-
-# Joints to freeze at 0 with high KP
-FROZEN_JOINTS = [13, 14, 20, 21, 27, 28]
-FROZEN_KP = 500.0
-FROZEN_KD = 5.0
-
-# =============================================================================
-# QUATERNION UTILITIES
-# =============================================================================
-
-def quat_inverse(q):
-    return np.concatenate((q[:, 0:1], -q[:, 1:]), axis=1)
-
-def quat_mul(a, b):
-    a, b = a.reshape(-1, 4), b.reshape(-1, 4)
-    w1, x1, y1, z1 = a[..., 0], a[..., 1], a[..., 2], a[..., 3]
-    w2, x2, y2, z2 = b[..., 0], b[..., 1], b[..., 2], b[..., 3]
-    ww = (z1 + x1) * (x2 + y2)
-    yy = (w1 - y1) * (w2 + z2)
-    zz = (w1 + y1) * (w2 - z2)
-    xx = ww + yy + zz
-    qq = 0.5 * (xx + (z1 - x1) * (x2 - y2))
-    w = qq - ww + (z1 - y1) * (y2 - z2)
-    x = qq - xx + (x1 + w1) * (x2 + w2)
-    y = qq - yy + (w1 - x1) * (y2 + z2)
-    z = qq - zz + (z1 + y1) * (w2 - x2)
-    return np.stack([w, x, y, z]).T.reshape(a.shape)
-
-def subtract_frame_transforms(q01, q02):
-    return quat_mul(quat_inverse(q01), q02)
-
-def matrix_from_quat(q):
-    r, i, j, k = q[..., 0], q[..., 1], q[..., 2], q[..., 3]
-    two_s = 2.0 / (q * q).sum(-1)
-    o = np.stack((
-        1 - two_s * (j*j + k*k), two_s * (i*j - k*r), two_s * (i*k + j*r),
-        two_s * (i*j + k*r), 1 - two_s * (i*i + k*k), two_s * (j*k - i*r),
-        two_s * (i*k - j*r), two_s * (j*k + i*r), 1 - two_s * (i*i + j*j),
-    ), -1)
-    return o.reshape(q.shape[:-1] + (3, 3))
-
-def xyzw_to_wxyz(xyzw):
-    return np.concatenate([xyzw[:, -1:], xyzw[:, :3]], axis=1)
-
-def quat_to_rpy(q):
-    w, x, y, z = q
-    roll = np.arctan2(2*(w*x + y*z), 1 - 2*(x**2 + y**2))
-    pitch = np.arcsin(np.clip(2*(w*y - z*x), -1, 1))
-    yaw = np.arctan2(2*(w*z + x*y), 1 - 2*(y**2 + z**2))
-    return roll, pitch, yaw
-
-def rpy_to_quat(rpy):
-    roll, pitch, yaw = rpy
-    cy, sy = np.cos(yaw*0.5), np.sin(yaw*0.5)
-    cp, sp = np.cos(pitch*0.5), np.sin(pitch*0.5)
-    cr, sr = np.cos(roll*0.5), np.sin(roll*0.5)
-    return np.array([cr*cp*cy + sr*sp*sy, sr*cp*cy - cr*sp*sy,
-                     cr*sp*cy + sr*cp*sy, cr*cp*sy - sr*sp*cy])
-
-# =============================================================================
-# PINOCCHIO FK
-# =============================================================================
-
-DOF_NAMES = (
-    "left_hip_pitch_joint", "left_hip_roll_joint", "left_hip_yaw_joint",
-    "left_knee_joint", "left_ankle_pitch_joint", "left_ankle_roll_joint",
-    "right_hip_pitch_joint", "right_hip_roll_joint", "right_hip_yaw_joint",
-    "right_knee_joint", "right_ankle_pitch_joint", "right_ankle_roll_joint",
-    "waist_yaw_joint", "waist_roll_joint", "waist_pitch_joint",
-    "left_shoulder_pitch_joint", "left_shoulder_roll_joint", "left_shoulder_yaw_joint", "left_elbow_joint",
-    "left_wrist_roll_joint", "left_wrist_pitch_joint", "left_wrist_yaw_joint",
-    "right_shoulder_pitch_joint", "right_shoulder_roll_joint", "right_shoulder_yaw_joint", "right_elbow_joint",
-    "right_wrist_roll_joint", "right_wrist_pitch_joint", "right_wrist_yaw_joint",
-)
-
-
-class PinocchioFK:
-    """Pinocchio forward kinematics for torso_link orientation."""
-
-    def __init__(self, urdf_text: str):
-        root = ElementTree.fromstring(urdf_text)
-        for parent in root.iter():
-            for child in list(parent):
-                if child.tag.split("}")[-1] in {"visual", "collision"}:
-                    parent.remove(child)
-        xml_text = '<?xml version="1.0"?>\n' + ElementTree.tostring(root, encoding="unicode")
-
-        self.model = pin.buildModelFromXML(xml_text, pin.JointModelFreeFlyer())
-        self.data = self.model.createData()
-
-        pin_names = [n for n in self.model.names if n not in ["universe", "root_joint"]]
-        self.idx_map = np.array([DOF_NAMES.index(n) for n in pin_names])
-        self.ref_frame_id = self.model.getFrameId("torso_link")
-        logger.info(f"Pinocchio FK: {len(pin_names)} joints, torso_link frame={self.ref_frame_id}")
-
-    def get_torso_quat(self, pos, quat_wxyz, dof_pos):
-        """Get torso_link orientation in world frame."""
-        quat_xyzw = np.array([quat_wxyz[1], quat_wxyz[2], quat_wxyz[3], quat_wxyz[0]])
-        config = np.concatenate([pos, quat_xyzw, dof_pos[self.idx_map]])
-        pin.framesForwardKinematics(self.model, self.data, config)
-        coeffs = pin.Quaternion(self.data.oMf[self.ref_frame_id].rotation).coeffs()
-        return np.array([coeffs[3], coeffs[0], coeffs[1], coeffs[2]]).reshape(1, 4)
-
-
-# =============================================================================
-# DANCE CONTROLLER
-# =============================================================================
-
-class DanceController:
-    """
-    Handles WBT dance policy for the Unitree G1 robot.
-
-    This controller manages:
-    - 29-joint observation processing
-    - Pinocchio FK for torso orientation
-    - Policy inference with motion data from ONNX
-    """
-
-    def __init__(self, policy, robot, pinocchio_fk, motor_kp, motor_kd, action_scale):
-        self.policy = policy
-        self.robot = robot
-        self.pinocchio_fk = pinocchio_fk
-        self.motor_kp = motor_kp
-        self.motor_kd = motor_kd
-        self.action_scale = action_scale
-
-        self.obs_dim = policy.get_inputs()[0].shape[1]
-        self.last_action = np.zeros((1, NUM_DOFS), dtype=np.float32)
-        self.motion_command = None
-        self.ref_quat_xyzw = None
-        self.timestep = 0
-        self.yaw_offset = 0.0
-
-        # Get initial motion data from ONNX
-        dummy = np.zeros((1, self.obs_dim), dtype=np.float32)
-        outs = self.policy.run(["joint_pos", "joint_vel", "ref_quat_xyzw"],
-                              {"obs": dummy, "time_step": np.array([[0]], dtype=np.float32)})
-        self.motion_command = np.concatenate(outs[0:2], axis=1)
-        self.ref_quat_xyzw = outs[2]
-        self.motion_start_pose = outs[0].flatten()
-
-        # Thread management
-        self.dance_running = False
-        self.dance_thread = None
-
-        logger.info(f"DanceController: obs_dim={self.obs_dim}, action_scale={action_scale}")
-
-    def capture_yaw_offset(self):
-        """Capture robot's current yaw for relative tracking."""
-        robot_state = self.robot.lowstate_buffer.get_data()
-        if robot_state and self.pinocchio_fk:
-            quat = np.array(robot_state.imu_state.quaternion, dtype=np.float32)
-            dof = np.array([robot_state.motor_state[i].q for i in range(NUM_DOFS)], dtype=np.float32)
-            torso_q = self.pinocchio_fk.get_torso_quat(np.zeros(3), quat, dof)
-            _, _, self.yaw_offset = quat_to_rpy(torso_q.flatten())
-            logger.info(f"Captured yaw offset: {np.degrees(self.yaw_offset):.1f}°")
-
-    def _remove_yaw_offset(self, quat_wxyz):
-        """Remove stored yaw offset from orientation."""
-        if abs(self.yaw_offset) < 1e-6:
-            return quat_wxyz
-        yaw_q = rpy_to_quat((0, 0, -self.yaw_offset)).reshape(1, 4)
-        return quat_mul(yaw_q, quat_wxyz)
-
-    def run_step(self):
-        """Single dance step - reads state, runs policy, sends commands."""
-        robot_state = self.robot.lowstate_buffer.get_data()
-        if robot_state is None:
-            return
-
-        # Read robot state
-        quat = np.array(robot_state.imu_state.quaternion, dtype=np.float32)
-        ang_vel = np.array(robot_state.imu_state.gyroscope, dtype=np.float32)
-        dof_pos = np.array([robot_state.motor_state[i].q for i in range(NUM_DOFS)], dtype=np.float32)
-        dof_vel = np.array([robot_state.motor_state[i].dq for i in range(NUM_DOFS)], dtype=np.float32)
-
-        # Compute motion_ref_ori_b using FK
-        if self.pinocchio_fk:
-            torso_q = self.pinocchio_fk.get_torso_quat(np.zeros(3), quat, dof_pos)
-            torso_q = self._remove_yaw_offset(torso_q)
-            motion_ori = xyzw_to_wxyz(self.ref_quat_xyzw)
-            rel_quat = subtract_frame_transforms(torso_q, motion_ori)
-            ori_b = matrix_from_quat(rel_quat)[..., :2].reshape(1, -1)
-        else:
-            ori_b = np.zeros((1, 6), dtype=np.float32)
-
-        dof_rel = (dof_pos - DEFAULT_DOF_POS).reshape(1, -1)
-
-        # Build observation (alphabetical order)
-        obs_dict = {
-            "actions": self.last_action,
-            "base_ang_vel": ang_vel.reshape(1, 3),
-            "dof_pos": dof_rel,
-            "dof_vel": dof_vel.reshape(1, -1),
-            "motion_command": self.motion_command,
-            "motion_ref_ori_b": ori_b,
-        }
-        obs = np.concatenate([obs_dict[k].astype(np.float32) for k in sorted(obs_dict.keys())], axis=1)
-        obs = np.clip(obs, -100, 100)
-
-        # Run policy
-        outs = self.policy.run(["actions", "joint_pos", "joint_vel", "ref_quat_xyzw"],
-                              {"obs": obs, "time_step": np.array([[self.timestep]], dtype=np.float32)})
-
-        action = np.clip(outs[0], -100, 100)
-        self.motion_command = np.concatenate(outs[1:3], axis=1)
-        self.ref_quat_xyzw = outs[3]
-        self.last_action = action.copy()
-
-        # Compute target positions
-        target_pos = DEFAULT_DOF_POS + action.flatten() * self.action_scale
-
-        # Send commands
-        for i in range(NUM_DOFS):
-            if i in FROZEN_JOINTS:
-                self.robot.msg.motor_cmd[i].q = 0.0
-                self.robot.msg.motor_cmd[i].kp = FROZEN_KP
-                self.robot.msg.motor_cmd[i].kd = FROZEN_KD
-            else:
-                self.robot.msg.motor_cmd[i].q = float(target_pos[i])
-                self.robot.msg.motor_cmd[i].kp = self.motor_kp[i]
-                self.robot.msg.motor_cmd[i].kd = self.motor_kd[i]
-            self.robot.msg.motor_cmd[i].qd = 0
-            self.robot.msg.motor_cmd[i].tau = 0
-
-        self.robot.send_action(self.robot.msg)
-        self.timestep += 1
-
-    def _dance_thread_loop(self):
-        """Background thread that runs the dance policy."""
-        logger.info("Dance thread started")
-        while self.dance_running:
-            start_time = time.time()
-            try:
-                self.run_step()
-            except Exception as e:
-                logger.error(f"Error in dance loop: {e}")
-                import traceback
-                traceback.print_exc()
-
-            elapsed = time.time() - start_time
-            sleep_time = max(0, CONTROL_DT - elapsed)
-            time.sleep(sleep_time)
-        logger.info("Dance thread stopped")
-
-    def start_dance_thread(self):
-        """Start the dance control thread."""
-        if self.dance_running:
-            logger.warning("Dance thread already running")
-            return
-
-        # Reset state for fresh start
-        self.timestep = 0
-        self.last_action.fill(0)
-
-        # Re-get initial motion data
-        dummy = np.zeros((1, self.obs_dim), dtype=np.float32)
-        outs = self.policy.run(["joint_pos", "joint_vel", "ref_quat_xyzw"],
-                              {"obs": dummy, "time_step": np.array([[0]], dtype=np.float32)})
-        self.motion_command = np.concatenate(outs[0:2], axis=1)
-        self.ref_quat_xyzw = outs[2]
-
-        self.capture_yaw_offset()
-
-        logger.info("Starting dance control thread...")
-        self.dance_running = True
-        self.dance_thread = threading.Thread(target=self._dance_thread_loop, daemon=True)
-        self.dance_thread.start()
-
-    def stop_dance_thread(self):
-        """Stop the dance control thread."""
-        if not self.dance_running:
-            return
-
-        logger.info("Stopping dance control thread...")
-        self.dance_running = False
-        if self.dance_thread:
-            self.dance_thread.join(timeout=2.0)
-        logger.info("Dance control thread stopped")
-
-    def reset_to_motion_pose(self, duration: float = 3.0):
-        """Move robot to initial motion pose over given duration."""
-        logger.info(f"Moving to dance start pose ({duration}s)...")
-
-        robot_state = self.robot.lowstate_buffer.get_data()
-        init_pos = np.array([robot_state.motor_state[i].q for i in range(NUM_DOFS)], dtype=np.float32)
-        target_pos = self.motion_start_pose
-
-        num_steps = int(duration / CONTROL_DT)
-        for step in range(num_steps):
-            alpha = step / num_steps
-            interp = init_pos * (1 - alpha) + target_pos * alpha
-
-            for i in range(NUM_DOFS):
-                if i in FROZEN_JOINTS:
-                    self.robot.msg.motor_cmd[i].q = 0.0
-                    self.robot.msg.motor_cmd[i].kp = FROZEN_KP
-                    self.robot.msg.motor_cmd[i].kd = FROZEN_KD
-                else:
-                    self.robot.msg.motor_cmd[i].q = float(interp[i])
-                    self.robot.msg.motor_cmd[i].kp = STIFF_KP[i]
-                    self.robot.msg.motor_cmd[i].kd = STIFF_KD[i]
-                self.robot.msg.motor_cmd[i].qd = 0
-                self.robot.msg.motor_cmd[i].tau = 0
-
-            self.robot.msg.crc = self.robot.crc.Crc(self.robot.msg)
-            self.robot.lowcmd_publisher.Write(self.robot.msg)
-            time.sleep(CONTROL_DT)
-
-        logger.info("At dance start pose!")
-
-
-# =============================================================================
-# MAIN
-# =============================================================================
-
-def load_dance_policy(onnx_path: str):
-    """Load dance policy and extract metadata."""
-    logger.info(f"Loading dance policy: {onnx_path}")
-
-    policy = ort.InferenceSession(onnx_path)
-    model = onnx.load(onnx_path)
-    metadata = {p.key: json.loads(p.value) for p in model.metadata_props}
-
-    motor_kp = np.array(metadata.get("kp", STIFF_KP), dtype=np.float32)
-    motor_kd = np.array(metadata.get("kd", STIFF_KD), dtype=np.float32)
-    action_scale = float(metadata.get("action_scale", 1.0))
-    urdf_text = metadata.get("robot_urdf", None)
-
-    logger.info(f"  Obs dim: {policy.get_inputs()[0].shape[1]}")
-    logger.info(f"  Action scale: {action_scale}")
-    logger.info(f"  KP range: [{motor_kp.min():.1f}, {motor_kp.max():.1f}]")
-
-    # Build Pinocchio FK if URDF available
-    pinocchio_fk = None
-    if urdf_text:
-        logger.info("  Building Pinocchio FK from URDF...")
-        pinocchio_fk = PinocchioFK(urdf_text)
-    else:
-        logger.warning("  No URDF in metadata - FK will not work!")
-
-    return policy, pinocchio_fk, motor_kp, motor_kd, action_scale
-
-
-def main():
-    parser = argparse.ArgumentParser(description="WBT Dance Policy for Unitree G1")
-    parser.add_argument("--onnx", type=str, default=DANCE_ONNX_PATH, help="Path to dance ONNX model")
-    parser.add_argument("--sim", action="store_true", help="Run in simulation mode")
-    args = parser.parse_args()
-
-    print("=" * 70)
-    print("💃 WBT DANCE POLICY")
-    print("=" * 70)
-
-    # Load policy
-    policy, pinocchio_fk, motor_kp, motor_kd, action_scale = load_dance_policy(args.onnx)
-
-    # Initialize robot
-    logger.info("Initializing robot...")
-    config = UnitreeG1Config()
-    robot = UnitreeG1(config)
-    logger.info("Robot connected!")
-
-    # Create controller
-    controller = DanceController(policy, robot, pinocchio_fk, motor_kp, motor_kd, action_scale)
-
-    try:
-        # Move to start pose
-        controller.reset_to_motion_pose(duration=3.0)
-
-        # Start dancing
-        controller.start_dance_thread()
-
-        logger.info("Dancing! Press Ctrl+C to stop.")
-        print("-" * 70)
-
-        # Log status periodically
-        while True:
-            time.sleep(2.0)
-            logger.info(f"timestep={controller.timestep}")
-
-    except KeyboardInterrupt:
-        print("\n\nStopping...")
-    finally:
-        controller.stop_dance_thread()
-        robot.disconnect()
-
-    print("\nDone!")
-
-
-if __name__ == "__main__":
-    main()

examples/unitree_g1/gr00t_locomotion.py

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

examples/unitree_g1/holosoma_locomotion.py

@@ -1,479 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Example: Holosoma Whole-Body Locomotion (23-DOF and 29-DOF)
-
-This example demonstrates loading Holosoma whole-body locomotion policies
-and running them on the Unitree G1 robot.
-
-Supports both:
-- 23-DOF native policies (82D observations, 23D actions)
-- 29-DOF policies (100D observations, 29D actions)
-"""
-
-import argparse
-import logging
-import threading
-import time
-
-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
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# =============================================================================
-# 29-DOF Configuration
-# =============================================================================
-# fmt: off
-HOLOSOMA_29DOF_DEFAULT_ANGLES = np.array([
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,  # left leg
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,  # right leg
-    0.0, 0.0, 0.0,                          # waist (yaw, roll, pitch)
-    0.2, 0.2, 0.0, 0.6, 0.0, 0.0, 0.0,     # left arm
-    0.2, -0.2, 0.0, 0.6, 0.0, 0.0, 0.0,    # right arm
-], dtype=np.float32)
-
-HOLOSOMA_29DOF_KP = np.array([
-    40.179238471, 99.098427777, 40.179238471, 99.098427777, 28.501246196, 28.501246196,  # left leg
-    40.179238471, 99.098427777, 40.179238471, 99.098427777, 28.501246196, 28.501246196,  # right leg
-    40.179238471, 28.501246196, 28.501246196,  # waist
-    14.250623098, 14.250623098, 14.250623098, 14.250623098, 14.250623098, 16.778327481, 16.778327481,  # left arm
-    14.250623098, 14.250623098, 14.250623098, 14.250623098, 14.250623098, 16.778327481, 16.778327481,  # right arm
-], dtype=np.float32)
-
-HOLOSOMA_29DOF_KD = np.array([
-    2.557889765, 6.308801854, 2.557889765, 6.308801854, 1.814445687, 1.814445687,  # left leg
-    2.557889765, 6.308801854, 2.557889765, 6.308801854, 1.814445687, 1.814445687,  # right leg
-    2.557889765, 1.814445687, 1.814445687,  # waist
-    0.907222843, 0.907222843, 0.907222843, 0.907222843, 0.907222843, 1.068141502, 1.068141502,  # left arm
-    0.907222843, 0.907222843, 0.907222843, 0.907222843, 0.907222843, 1.068141502, 1.068141502,  # right arm
-], dtype=np.float32)
-
-# =============================================================================
-# 23-DOF Configuration (native G1-23: no waist_roll/pitch, no wrist_pitch/yaw)
-# Derived from 29-DOF Holosoma values
-# =============================================================================
-# Joint order: 6 left leg, 6 right leg, 1 waist_yaw, 5 left arm, 5 right arm
-HOLOSOMA_23DOF_DEFAULT_ANGLES = np.array([
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,  # left leg (from 29-DOF)
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,  # right leg (from 29-DOF)
-    0.0,                                    # waist_yaw only (from 29-DOF)
-    0.2, 0.2, 0.0, 0.6, 0.0,               # left arm first 5 joints (from 29-DOF)
-    0.2, -0.2, 0.0, 0.6, 0.0,              # right arm first 5 joints (from 29-DOF)
-], dtype=np.float32)
-
-HOLOSOMA_23DOF_KP = np.array([
-    40.179238471, 99.098427777, 40.179238471, 99.098427777, 28.501246196, 28.501246196,  # left leg
-    40.179238471, 99.098427777, 40.179238471, 99.098427777, 28.501246196, 28.501246196,  # right leg
-    40.179238471,                                                                         # waist_yaw
-    14.250623098, 14.250623098, 14.250623098, 14.250623098, 14.250623098,                 # left arm
-    14.250623098, 14.250623098, 14.250623098, 14.250623098, 14.250623098,                 # right arm
-], dtype=np.float32)
-
-HOLOSOMA_23DOF_KD = np.array([
-    2.557889765, 6.308801854, 2.557889765, 6.308801854, 1.814445687, 1.814445687,  # left leg
-    2.557889765, 6.308801854, 2.557889765, 6.308801854, 1.814445687, 1.814445687,  # right leg
-    2.557889765,                                                                    # waist_yaw
-    0.907222843, 0.907222843, 0.907222843, 0.907222843, 0.907222843,               # left arm
-    0.907222843, 0.907222843, 0.907222843, 0.907222843, 0.907222843,               # right arm
-], dtype=np.float32)
-
-# Maps 23-DOF policy index → 29-DOF motor index
-# 23-DOF: legs(0-11), waist_yaw(12), L_arm(13-17), R_arm(18-22)
-# 29-DOF: legs(0-11), waist(12-14), L_arm(15-21), R_arm(22-28)
-DOF_23_TO_MOTOR_MAP = [
-    0, 1, 2, 3, 4, 5,       # left leg → motor 0-5
-    6, 7, 8, 9, 10, 11,     # right leg → motor 6-11
-    12,                      # waist_yaw → motor 12
-    15, 16, 17, 18, 19,     # left arm (skip wrist_pitch/yaw) → motor 15-19
-    22, 23, 24, 25, 26,     # right arm (skip wrist_pitch/yaw) → motor 22-26
-]
-# fmt: on
-
-# Control parameters
-LOCOMOTION_CONTROL_DT = 0.02  # 50Hz
-LOCOMOTION_ACTION_SCALE = 0.25
-ANG_VEL_SCALE = 0.25
-DOF_POS_SCALE = 1.0
-DOF_VEL_SCALE = 0.05
-GAIT_PERIOD = 1.0
-
-DEFAULT_HOLOSOMA_REPO_ID = "nepyope/holosoma_locomotion"
-
-
-def load_holosoma_policy(
-    repo_id: str = DEFAULT_HOLOSOMA_REPO_ID,
-    policy_name: str = "fastsac",
-    local_path: str | None = None,
-) -> tuple[ort.InferenceSession, int]:
-    """Load Holosoma policy and detect observation dimension.
-
-    Returns:
-        (policy, obs_dim) tuple where obs_dim is 82 (23-DOF) or 100 (29-DOF)
-    """
-    if local_path is not None:
-        logger.info(f"Loading policy from local path: {local_path}")
-        policy_path = local_path
-    else:
-        logger.info(f"Loading policy from Hugging Face Hub: {repo_id}")
-        policy_path = hf_hub_download(repo_id=repo_id, filename=f"{policy_name}_g1_29dof.onnx")
-
-    policy = ort.InferenceSession(policy_path)
-
-    # Detect observation dimension from model input shape
-    input_shape = policy.get_inputs()[0].shape
-    obs_dim = input_shape[1] if len(input_shape) > 1 else input_shape[0]
-
-    logger.info(f"Policy loaded successfully")
-    logger.info(f"  Input: {policy.get_inputs()[0].name}, shape: {input_shape} → obs_dim={obs_dim}")
-    logger.info(f"  Output: {policy.get_outputs()[0].name}, shape: {policy.get_outputs()[0].shape}")
-
-    return policy, obs_dim
-
-
-class HolosomaLocomotionController:
-    """
-    Handles Holosoma whole-body locomotion for Unitree G1.
-    Supports both 23-DOF (82D obs) and 29-DOF (100D obs) policies.
-    """
-
-    def __init__(self, policy, robot, config, obs_dim: int = 100):
-        self.policy = policy
-        self.robot = robot
-        self.config = config
-        self.obs_dim = obs_dim
-
-        # Detect policy type from observation dimension
-        self.is_23dof = (obs_dim == 82)
-        self.num_dof = 23 if self.is_23dof else 29
-
-        # Velocity commands
-        self.locomotion_cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32)
-
-        # State variables sized for policy type
-        self.qj = np.zeros(self.num_dof, dtype=np.float32)
-        self.dqj = np.zeros(self.num_dof, dtype=np.float32)
-        self.locomotion_action = np.zeros(self.num_dof, dtype=np.float32)
-        self.locomotion_obs = np.zeros(obs_dim, dtype=np.float32)
-        self.last_unscaled_action = np.zeros(self.num_dof, dtype=np.float32)
-
-        # Select config based on DOF
-        if self.is_23dof:
-            self.default_angles = HOLOSOMA_23DOF_DEFAULT_ANGLES
-            self.kp = HOLOSOMA_23DOF_KP
-            self.kd = HOLOSOMA_23DOF_KD
-            self.motor_map = DOF_23_TO_MOTOR_MAP
-        else:
-            self.default_angles = HOLOSOMA_29DOF_DEFAULT_ANGLES
-            self.kp = HOLOSOMA_29DOF_KP
-            self.kd = HOLOSOMA_29DOF_KD
-            self.motor_map = list(range(29))  # Identity map for 29-DOF
-
-        # Phase state for gait
-        self.phase = np.zeros((1, 2), dtype=np.float32)
-        self.phase[0, 0] = 0.0
-        self.phase[0, 1] = np.pi
-        self.phase_dt = 2 * np.pi / (50.0 * GAIT_PERIOD)
-        self.is_standing = False
-
-        self.counter = 0
-        self.locomotion_running = False
-        self.locomotion_thread = None
-
-        logger.info(f"HolosomaLocomotionController initialized")
-        logger.info(f"  Mode: {'23-DOF (82D obs)' if self.is_23dof else '29-DOF (100D obs)'}")
-        logger.info(f"  Action dim: {self.num_dof}")
-
-    def holosoma_locomotion_run(self):
-        """Main locomotion loop - handles both 23-DOF and 29-DOF."""
-        self.counter += 1
-
-        if self.counter == 1:
-            print("\n" + "=" * 60)
-            print(f"🚀 RUNNING HOLOSOMA {self.num_dof}-DOF LOCOMOTION POLICY")
-            print(f"   {self.obs_dim}D observations → {self.num_dof}D actions")
-            print("=" * 60 + "\n")
-
-        robot_state = self.robot.get_observation()
-        if robot_state is None:
-            return
-
-        # Remote controller
-        if robot_state.wireless_remote is not None:
-            self.robot.remote_controller.set(robot_state.wireless_remote)
-        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
-
-        # Deadzone
-        ly = self.robot.remote_controller.ly if abs(self.robot.remote_controller.ly) > 0.1 else 0.0
-        lx = self.robot.remote_controller.lx if abs(self.robot.remote_controller.lx) > 0.1 else 0.0
-        rx = self.robot.remote_controller.rx if abs(self.robot.remote_controller.rx) > 0.1 else 0.0
-
-        self.locomotion_cmd[0] = ly
-        self.locomotion_cmd[1] = -lx
-        self.locomotion_cmd[2] = -rx
-
-        # Read joint states using motor map
-        for i in range(self.num_dof):
-            motor_idx = self.motor_map[i]
-            self.qj[i] = robot_state.motor_state[motor_idx].q
-            self.dqj[i] = robot_state.motor_state[motor_idx].dq
-
-        # IMU
-        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 observations
-        qj_obs = (self.qj - self.default_angles) * DOF_POS_SCALE
-        dqj_obs = self.dqj * DOF_VEL_SCALE
-        ang_vel_scaled = ang_vel * ANG_VEL_SCALE
-
-        # Phase update
-        cmd_norm = np.linalg.norm(self.locomotion_cmd[:2])
-        ang_cmd_norm = np.abs(self.locomotion_cmd[2])
-
-        if cmd_norm < 0.01 and ang_cmd_norm < 0.01:
-            self.phase[0, :] = np.pi * np.ones(2)
-            self.is_standing = True
-        elif self.is_standing:
-            self.phase = np.array([[0.0, np.pi]], dtype=np.float32)
-            self.is_standing = False
-        else:
-            phase_tp1 = self.phase + self.phase_dt
-            self.phase = np.fmod(phase_tp1 + np.pi, 2 * np.pi) - np.pi
-
-        sin_phase = np.sin(self.phase[0, :])
-        cos_phase = np.cos(self.phase[0, :])
-
-        # Build observation (format depends on DOF)
-        if self.is_23dof:
-            # 82D: [23 actions, 3 ang_vel, 1 cmd_yaw, 2 cmd_lin, 2 cos, 23 pos, 23 vel, 3 grav, 2 sin]
-            self.locomotion_obs[0:23] = self.last_unscaled_action
-            self.locomotion_obs[23:26] = ang_vel_scaled
-            self.locomotion_obs[26] = self.locomotion_cmd[2]
-            self.locomotion_obs[27:29] = self.locomotion_cmd[:2]
-            self.locomotion_obs[29:31] = cos_phase
-            self.locomotion_obs[31:54] = qj_obs
-            self.locomotion_obs[54:77] = dqj_obs
-            self.locomotion_obs[77:80] = gravity_orientation
-            self.locomotion_obs[80:82] = sin_phase
-        else:
-            # 100D: [29 actions, 3 ang_vel, 1 cmd_yaw, 2 cmd_lin, 2 cos, 29 pos, 29 vel, 3 grav, 2 sin]
-            self.locomotion_obs[0:29] = self.last_unscaled_action
-            self.locomotion_obs[29:32] = ang_vel_scaled
-            self.locomotion_obs[32] = self.locomotion_cmd[2]
-            self.locomotion_obs[33:35] = self.locomotion_cmd[:2]
-            self.locomotion_obs[35:37] = cos_phase
-            self.locomotion_obs[37:66] = qj_obs
-            self.locomotion_obs[66:95] = dqj_obs
-            self.locomotion_obs[95:98] = gravity_orientation
-            self.locomotion_obs[98:100] = sin_phase
-
-        # Policy inference
-        obs_input = self.locomotion_obs.reshape(1, -1).astype(np.float32)
-        ort_inputs = {self.policy.get_inputs()[0].name: obs_input}
-        ort_outs = self.policy.run(None, ort_inputs)
-
-        raw_action = ort_outs[0].squeeze()
-        clipped_action = np.clip(raw_action, -100.0, 100.0)
-
-        self.last_unscaled_action = clipped_action.copy()
-        self.locomotion_action = clipped_action * LOCOMOTION_ACTION_SCALE
-
-        # Debug
-        if self.counter <= 3:
-            print(f"\n[Holosoma Debug #{self.counter}]")
-            print(f"  Phase: ({self.phase[0, 0]:.3f}, {self.phase[0, 1]:.3f})")
-            print(f"  Cmd: ({self.locomotion_cmd[0]:.2f}, {self.locomotion_cmd[1]:.2f}, {self.locomotion_cmd[2]:.2f})")
-            print(f"  Action range: [{raw_action.min():.3f}, {raw_action.max():.3f}]")
-
-        # Compute target positions
-        target_dof_pos = self.default_angles + self.locomotion_action
-
-        # Send commands to motors via motor map
-        for i in range(self.num_dof):
-            motor_idx = self.motor_map[i]
-            self.robot.msg.motor_cmd[motor_idx].q = target_dof_pos[i]
-            self.robot.msg.motor_cmd[motor_idx].qd = 0
-            self.robot.msg.motor_cmd[motor_idx].kp = self.kp[i]
-            self.robot.msg.motor_cmd[motor_idx].kd = self.kd[i]
-            self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-        # For 23-DOF: zero out missing joints (waist_roll/pitch, wrist_pitch/yaw)
-        if self.is_23dof:
-            missing_motors = [13, 14, 20, 21, 27, 28]  # waist_roll, waist_pitch, wrist_pitch/yaw
-            for motor_idx in missing_motors:
-                self.robot.msg.motor_cmd[motor_idx].q = 0.0
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = 40.0
-                self.robot.msg.motor_cmd[motor_idx].kd = 2.0
-                self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-        self.robot.send_action(self.robot.msg)
-
-    def _locomotion_thread_loop(self):
-        logger.info("Locomotion thread started")
-        while self.locomotion_running:
-            start_time = time.time()
-            try:
-                self.holosoma_locomotion_run()
-            except Exception as e:
-                logger.error(f"Error in locomotion loop: {e}")
-                import traceback
-                traceback.print_exc()
-
-            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 joints to default position."""
-        logger.info(f"Moving {self.num_dof} joints to default position...")
-
-        total_time = 3.0
-        num_step = int(total_time / self.robot.control_dt)
-
-        robot_state = self.robot.get_observation()
-
-        # Record current positions
-        init_dof_pos = np.zeros(self.num_dof, dtype=np.float32)
-        for i in range(self.num_dof):
-            motor_idx = self.motor_map[i]
-            init_dof_pos[i] = robot_state.motor_state[motor_idx].q
-
-        # Interpolate to target
-        for step in range(num_step):
-            alpha = step / num_step
-            for i in range(self.num_dof):
-                motor_idx = self.motor_map[i]
-                target = self.default_angles[i]
-                self.robot.msg.motor_cmd[motor_idx].q = init_dof_pos[i] * (1 - alpha) + target * alpha
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = self.kp[i]
-                self.robot.msg.motor_cmd[motor_idx].kd = self.kd[i]
-                self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-            # Zero missing joints for 23-DOF
-            if self.is_23dof:
-                for motor_idx in [13, 14, 20, 21, 27, 28]:
-                    self.robot.msg.motor_cmd[motor_idx].q = 0.0
-                    self.robot.msg.motor_cmd[motor_idx].qd = 0
-                    self.robot.msg.motor_cmd[motor_idx].kp = 40.0
-                    self.robot.msg.motor_cmd[motor_idx].kd = 2.0
-                    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(f"Reached default position ({self.num_dof} joints)")
-
-        # Hold for 2 seconds
-        logger.info("Holding default position for 2 seconds...")
-        hold_steps = int(2.0 / self.robot.control_dt)
-        for _ in range(hold_steps):
-            for i in range(self.num_dof):
-                motor_idx = self.motor_map[i]
-                self.robot.msg.motor_cmd[motor_idx].q = self.default_angles[i]
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = self.kp[i]
-                self.robot.msg.motor_cmd[motor_idx].kd = self.kd[i]
-                self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-            if self.is_23dof:
-                for motor_idx in [13, 14, 20, 21, 27, 28]:
-                    self.robot.msg.motor_cmd[motor_idx].q = 0.0
-                    self.robot.msg.motor_cmd[motor_idx].qd = 0
-                    self.robot.msg.motor_cmd[motor_idx].kp = 40.0
-                    self.robot.msg.motor_cmd[motor_idx].kd = 2.0
-                    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("Ready to start locomotion!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Holosoma Locomotion Controller for Unitree G1")
-    parser.add_argument("--repo-id", type=str, default=DEFAULT_HOLOSOMA_REPO_ID)
-    parser.add_argument("--policy", type=str, default="fastsac", choices=["fastsac", "ppo"])
-    parser.add_argument("--local-path", type=str, default=None, help="Path to local ONNX file")
-    args = parser.parse_args()
-
-    # Load policy and detect dimensions
-    policy, obs_dim = load_holosoma_policy(
-        repo_id=args.repo_id,
-        policy_name=args.policy,
-        local_path=args.local_path,
-    )
-
-    # Initialize robot
-    config = UnitreeG1Config()
-    robot = UnitreeG1(config)
-
-    # Initialize controller with detected obs_dim
-    controller = HolosomaLocomotionController(
-        policy=policy,
-        robot=robot,
-        config=config,
-        obs_dim=obs_dim,
-    )
-
-    try:
-        #controller.reset_robot()
-        controller.start_locomotion_thread()
-
-        logger.info(f"Robot initialized with Holosoma {'23-DOF' if obs_dim == 82 else '29-DOF'} policy")
-        logger.info("Use remote controller: LY=fwd/back, LX=left/right, RX=rotate")
-        logger.info("Press Ctrl+C to stop")
-
-        while True:
-            time.sleep(1.0)
-    except KeyboardInterrupt:
-        print("\nStopping locomotion...")
-        controller.stop_locomotion_thread()
-        print("Done!")

examples/unitree_g1/locomotion_to_dance.py

@@ -1,607 +0,0 @@
-#!/usr/bin/env python3
-"""
-Locomotion ↔ Dance Toggle for Unitree G1
-
-Press Enter to instantly switch between locomotion and dance modes.
-- Starts in LOCOMOTION mode (joystick control)
-- Press Enter → DANCE mode (resets to frame 0)
-- Press Enter → LOCOMOTION mode
-- Repeat...
-
-Auto-recovery feature:
-- If robot tilts beyond threshold during dance, auto-switches to locomotion
-- When robot recovers (tilt below recovery threshold), resumes dance from where it left off
-
-Usage:
-    python examples/unitree_g1/locomotion_to_dance.py
-    python examples/unitree_g1/locomotion_to_dance.py --tilt-threshold 25 --recovery-threshold 10
-"""
-
-import argparse
-import json
-import logging
-import select
-import sys
-import threading
-import time
-from xml.etree import ElementTree
-
-import numpy as np
-import onnx
-import onnxruntime as ort
-import pinocchio as pin
-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
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# =============================================================================
-# CONFIGURATION
-# =============================================================================
-
-NUM_DOFS = 29
-CONTROL_DT = 0.02  # 50Hz
-
-# Locomotion config
-DEFAULT_HOLOSOMA_REPO_ID = "nepyope/holosoma_locomotion"
-LOCOMOTION_ACTION_SCALE = 0.25
-ANG_VEL_SCALE = 0.25
-DOF_POS_SCALE = 1.0
-DOF_VEL_SCALE = 0.05
-GAIT_PERIOD = 1.0
-
-# Dance config
-DANCE_ONNX_PATH = "examples/unitree_g1/fastsac_g1_29dof_dancing.onnx"
-FROZEN_JOINTS = [13, 14, 20, 21, 27, 28]
-FROZEN_KP = 500.0
-FROZEN_KD = 5.0
-
-# fmt: off
-# 29-DOF defaults (holosoma training)
-DEFAULT_29DOF_ANGLES = np.array([
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,  # left leg
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,  # right leg
-    0.0, 0.0, 0.0,                          # waist
-    0.2, 0.2, 0.0, 0.6, 0.0, 0.0, 0.0,     # left arm
-    0.2, -0.2, 0.0, 0.6, 0.0, 0.0, 0.0,    # right arm
-], dtype=np.float32)
-
-DEFAULT_29DOF_KP = np.array([
-    40.179, 99.098, 40.179, 99.098, 28.501, 28.501,
-    40.179, 99.098, 40.179, 99.098, 28.501, 28.501,
-    40.179, 28.501, 28.501,
-    14.251, 14.251, 14.251, 14.251, 14.251, 16.778, 16.778,
-    14.251, 14.251, 14.251, 14.251, 14.251, 16.778, 16.778,
-], dtype=np.float32)
-
-DEFAULT_29DOF_KD = np.array([
-    2.558, 6.309, 2.558, 6.309, 1.814, 1.814,
-    2.558, 6.309, 2.558, 6.309, 1.814, 1.814,
-    2.558, 1.814, 1.814,
-    0.907, 0.907, 0.907, 0.907, 0.907, 1.068, 1.068,
-    0.907, 0.907, 0.907, 0.907, 0.907, 1.068, 1.068,
-], dtype=np.float32)
-
-# 23-DOF config (no waist_roll/pitch, no wrist_pitch/yaw)
-DEFAULT_23DOF_ANGLES = np.array([
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,  # left leg
-    -0.312, 0.0, 0.0, 0.669, -0.363, 0.0,  # right leg
-    0.0,                                    # waist_yaw only
-    0.2, 0.2, 0.0, 0.6, 0.0,               # left arm (5 joints)
-    0.2, -0.2, 0.0, 0.6, 0.0,              # right arm (5 joints)
-], dtype=np.float32)
-
-DEFAULT_23DOF_KP = np.array([
-    40.179, 99.098, 40.179, 99.098, 28.501, 28.501,
-    40.179, 99.098, 40.179, 99.098, 28.501, 28.501,
-    40.179,
-    14.251, 14.251, 14.251, 14.251, 14.251,
-    14.251, 14.251, 14.251, 14.251, 14.251,
-], dtype=np.float32)
-
-DEFAULT_23DOF_KD = np.array([
-    2.558, 6.309, 2.558, 6.309, 1.814, 1.814,
-    2.558, 6.309, 2.558, 6.309, 1.814, 1.814,
-    2.558,
-    0.907, 0.907, 0.907, 0.907, 0.907,
-    0.907, 0.907, 0.907, 0.907, 0.907,
-], dtype=np.float32)
-
-# 23-DOF policy index → 29-DOF motor index
-DOF_23_TO_MOTOR = [
-    0, 1, 2, 3, 4, 5,       # left leg
-    6, 7, 8, 9, 10, 11,     # right leg
-    12,                      # waist_yaw
-    15, 16, 17, 18, 19,     # left arm (skip wrist_pitch/yaw)
-    22, 23, 24, 25, 26,     # right arm (skip wrist_pitch/yaw)
-]
-MISSING_23DOF_MOTORS = [13, 14, 20, 21, 27, 28]
-# fmt: on
-
-# =============================================================================
-# QUATERNION UTILITIES
-# =============================================================================
-
-def quat_inverse(q):
-    return np.concatenate((q[:, 0:1], -q[:, 1:]), axis=1)
-
-def quat_mul(a, b):
-    a, b = a.reshape(-1, 4), b.reshape(-1, 4)
-    w1, x1, y1, z1 = a[..., 0], a[..., 1], a[..., 2], a[..., 3]
-    w2, x2, y2, z2 = b[..., 0], b[..., 1], b[..., 2], b[..., 3]
-    ww = (z1 + x1) * (x2 + y2)
-    yy = (w1 - y1) * (w2 + z2)
-    zz = (w1 + y1) * (w2 - z2)
-    xx = ww + yy + zz
-    qq = 0.5 * (xx + (z1 - x1) * (x2 - y2))
-    w = qq - ww + (z1 - y1) * (y2 - z2)
-    x = qq - xx + (x1 + w1) * (x2 + w2)
-    y = qq - yy + (w1 - x1) * (y2 + z2)
-    z = qq - zz + (z1 + y1) * (w2 - x2)
-    return np.stack([w, x, y, z]).T.reshape(a.shape)
-
-def subtract_frame_transforms(q01, q02):
-    return quat_mul(quat_inverse(q01), q02)
-
-def matrix_from_quat(q):
-    r, i, j, k = q[..., 0], q[..., 1], q[..., 2], q[..., 3]
-    two_s = 2.0 / (q * q).sum(-1)
-    o = np.stack((
-        1 - two_s * (j*j + k*k), two_s * (i*j - k*r), two_s * (i*k + j*r),
-        two_s * (i*j + k*r), 1 - two_s * (i*i + k*k), two_s * (j*k - i*r),
-        two_s * (i*k - j*r), two_s * (j*k + i*r), 1 - two_s * (i*i + j*j),
-    ), -1)
-    return o.reshape(q.shape[:-1] + (3, 3))
-
-def xyzw_to_wxyz(xyzw):
-    return np.concatenate([xyzw[:, -1:], xyzw[:, :3]], axis=1)
-
-def quat_to_rpy(q):
-    w, x, y, z = q
-    roll = np.arctan2(2*(w*x + y*z), 1 - 2*(x**2 + y**2))
-    pitch = np.arcsin(np.clip(2*(w*y - z*x), -1, 1))
-    yaw = np.arctan2(2*(w*z + x*y), 1 - 2*(y**2 + z**2))
-    return roll, pitch, yaw
-
-def rpy_to_quat(rpy):
-    roll, pitch, yaw = rpy
-    cy, sy = np.cos(yaw*0.5), np.sin(yaw*0.5)
-    cp, sp = np.cos(pitch*0.5), np.sin(pitch*0.5)
-    cr, sr = np.cos(roll*0.5), np.sin(roll*0.5)
-    return np.array([cr*cp*cy + sr*sp*sy, sr*cp*cy - cr*sp*sy,
-                     cr*sp*cy + sr*cp*sy, cr*cp*sy - sr*sp*cy])
-
-# =============================================================================
-# PINOCCHIO FK
-# =============================================================================
-
-DOF_NAMES = (
-    "left_hip_pitch_joint", "left_hip_roll_joint", "left_hip_yaw_joint",
-    "left_knee_joint", "left_ankle_pitch_joint", "left_ankle_roll_joint",
-    "right_hip_pitch_joint", "right_hip_roll_joint", "right_hip_yaw_joint",
-    "right_knee_joint", "right_ankle_pitch_joint", "right_ankle_roll_joint",
-    "waist_yaw_joint", "waist_roll_joint", "waist_pitch_joint",
-    "left_shoulder_pitch_joint", "left_shoulder_roll_joint", "left_shoulder_yaw_joint", "left_elbow_joint",
-    "left_wrist_roll_joint", "left_wrist_pitch_joint", "left_wrist_yaw_joint",
-    "right_shoulder_pitch_joint", "right_shoulder_roll_joint", "right_shoulder_yaw_joint", "right_elbow_joint",
-    "right_wrist_roll_joint", "right_wrist_pitch_joint", "right_wrist_yaw_joint",
-)
-
-
-class PinocchioFK:
-    def __init__(self, urdf_text: str):
-        root = ElementTree.fromstring(urdf_text)
-        for parent in root.iter():
-            for child in list(parent):
-                if child.tag.split("}")[-1] in {"visual", "collision"}:
-                    parent.remove(child)
-        xml_text = '<?xml version="1.0"?>\n' + ElementTree.tostring(root, encoding="unicode")
-        self.model = pin.buildModelFromXML(xml_text, pin.JointModelFreeFlyer())
-        self.data = self.model.createData()
-        pin_names = [n for n in self.model.names if n not in ["universe", "root_joint"]]
-        self.idx_map = np.array([DOF_NAMES.index(n) for n in pin_names])
-        self.ref_frame_id = self.model.getFrameId("torso_link")
-
-    def get_torso_quat(self, pos, quat_wxyz, dof_pos):
-        quat_xyzw = np.array([quat_wxyz[1], quat_wxyz[2], quat_wxyz[3], quat_wxyz[0]])
-        config = np.concatenate([pos, quat_xyzw, dof_pos[self.idx_map]])
-        pin.framesForwardKinematics(self.model, self.data, config)
-        coeffs = pin.Quaternion(self.data.oMf[self.ref_frame_id].rotation).coeffs()
-        return np.array([coeffs[3], coeffs[0], coeffs[1], coeffs[2]]).reshape(1, 4)
-
-    def get_torso_tilt(self, pos, quat_wxyz, dof_pos):
-        """Get torso tilt angle from upright (degrees). Uses roll and pitch."""
-        torso_q = self.get_torso_quat(pos, quat_wxyz, dof_pos)
-        roll, pitch, _ = quat_to_rpy(torso_q.flatten())
-        # Tilt is the angle from vertical - combine roll and pitch
-        tilt_rad = np.sqrt(roll**2 + pitch**2)
-        return np.degrees(tilt_rad), np.degrees(roll), np.degrees(pitch)
-
-
-# =============================================================================
-# LOCOMOTION CONTROLLER
-# =============================================================================
-
-class LocomotionController:
-    """Holosoma whole-body locomotion (23-DOF or 29-DOF)."""
-
-    def __init__(self, policy, robot, obs_dim: int):
-        self.policy = policy
-        self.robot = robot
-        self.obs_dim = obs_dim
-
-        # Detect DOF mode
-        self.is_23dof = (obs_dim == 82)
-        self.num_dof = 23 if self.is_23dof else 29
-
-        if self.is_23dof:
-            self.default_angles = DEFAULT_23DOF_ANGLES
-            self.kp = DEFAULT_23DOF_KP
-            self.kd = DEFAULT_23DOF_KD
-            self.motor_map = DOF_23_TO_MOTOR
-            logger.info("Locomotion: 23-DOF (82D obs)")
-        else:
-            self.default_angles = DEFAULT_29DOF_ANGLES
-            self.kp = DEFAULT_29DOF_KP
-            self.kd = DEFAULT_29DOF_KD
-            self.motor_map = list(range(29))
-            logger.info("Locomotion: 29-DOF (100D obs)")
-
-        self.cmd = np.zeros(3, dtype=np.float32)
-        self.qj = np.zeros(self.num_dof, dtype=np.float32)
-        self.dqj = np.zeros(self.num_dof, dtype=np.float32)
-        self.obs = np.zeros(obs_dim, dtype=np.float32)
-        self.last_action = np.zeros(self.num_dof, dtype=np.float32)
-
-        self.phase = np.array([[0.0, np.pi]], dtype=np.float32)
-        self.phase_dt = 2 * np.pi / (50.0 * GAIT_PERIOD)
-        self.is_standing = True
-
-    def run_step(self):
-        """Single locomotion step."""
-        state = self.robot.lowstate_buffer.get_data()
-        if state is None:
-            return
-
-        # Joystick
-        if state.wireless_remote is not None:
-            self.robot.remote_controller.set(state.wireless_remote)
-
-        ly = self.robot.remote_controller.ly if abs(self.robot.remote_controller.ly) > 0.1 else 0.0
-        lx = self.robot.remote_controller.lx if abs(self.robot.remote_controller.lx) > 0.1 else 0.0
-        rx = self.robot.remote_controller.rx if abs(self.robot.remote_controller.rx) > 0.1 else 0.0
-        self.cmd[0], self.cmd[1], self.cmd[2] = ly, -lx, -rx
-
-        # Read joints via motor map
-        for i in range(self.num_dof):
-            self.qj[i] = state.motor_state[self.motor_map[i]].q
-            self.dqj[i] = state.motor_state[self.motor_map[i]].dq
-
-        # IMU
-        quat = state.imu_state.quaternion
-        ang_vel = np.array(state.imu_state.gyroscope, dtype=np.float32)
-        gravity = self.robot.get_gravity_orientation(quat)
-
-        # Scale
-        qj_obs = (self.qj - self.default_angles) * DOF_POS_SCALE
-        dqj_obs = self.dqj * DOF_VEL_SCALE
-        ang_vel_s = ang_vel * ANG_VEL_SCALE
-
-        # Phase
-        cmd_mag = np.linalg.norm(self.cmd[:2])
-        ang_mag = abs(self.cmd[2])
-        if cmd_mag < 0.01 and ang_mag < 0.01:
-            self.phase[0, :] = np.pi
-            self.is_standing = True
-        elif self.is_standing:
-            self.phase = np.array([[0.0, np.pi]], dtype=np.float32)
-            self.is_standing = False
-        else:
-            self.phase = np.fmod(self.phase + self.phase_dt + np.pi, 2*np.pi) - np.pi
-
-        sin_ph, cos_ph = np.sin(self.phase[0]), np.cos(self.phase[0])
-
-        # Build obs
-        if self.is_23dof:
-            self.obs[0:23] = self.last_action
-            self.obs[23:26] = ang_vel_s
-            self.obs[26] = self.cmd[2]
-            self.obs[27:29] = self.cmd[:2]
-            self.obs[29:31] = cos_ph
-            self.obs[31:54] = qj_obs
-            self.obs[54:77] = dqj_obs
-            self.obs[77:80] = gravity
-            self.obs[80:82] = sin_ph
-        else:
-            self.obs[0:29] = self.last_action
-            self.obs[29:32] = ang_vel_s
-            self.obs[32] = self.cmd[2]
-            self.obs[33:35] = self.cmd[:2]
-            self.obs[35:37] = cos_ph
-            self.obs[37:66] = qj_obs
-            self.obs[66:95] = dqj_obs
-            self.obs[95:98] = gravity
-            self.obs[98:100] = sin_ph
-
-        # Inference
-        obs_in = self.obs.reshape(1, -1).astype(np.float32)
-        ort_in = {self.policy.get_inputs()[0].name: obs_in}
-        raw_action = self.policy.run(None, ort_in)[0].squeeze()
-        clipped = np.clip(raw_action, -100.0, 100.0)
-        self.last_action = clipped.copy()
-        scaled = clipped * LOCOMOTION_ACTION_SCALE
-        target = self.default_angles + scaled
-
-        # Send commands
-        for i in range(self.num_dof):
-            motor_idx = self.motor_map[i]
-            self.robot.msg.motor_cmd[motor_idx].q = float(target[i])
-            self.robot.msg.motor_cmd[motor_idx].qd = 0
-            self.robot.msg.motor_cmd[motor_idx].kp = self.kp[i]
-            self.robot.msg.motor_cmd[motor_idx].kd = self.kd[i]
-            self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-        # Zero missing joints for 23-DOF
-        if self.is_23dof:
-            for idx in MISSING_23DOF_MOTORS:
-                self.robot.msg.motor_cmd[idx].q = 0.0
-                self.robot.msg.motor_cmd[idx].qd = 0
-                self.robot.msg.motor_cmd[idx].kp = 40.0
-                self.robot.msg.motor_cmd[idx].kd = 2.0
-                self.robot.msg.motor_cmd[idx].tau = 0
-
-        self.robot.send_action(self.robot.msg)
-
-    def reset(self):
-        """Reset state for fresh start."""
-        self.last_action.fill(0)
-        self.phase = np.array([[0.0, np.pi]], dtype=np.float32)
-        self.is_standing = True
-
-
-# =============================================================================
-# DANCE CONTROLLER
-# =============================================================================
-
-class DanceController:
-    """WBT dance policy with FK for torso tracking."""
-
-    def __init__(self, policy, robot, pinocchio_fk, motor_kp, motor_kd, action_scale):
-        self.policy = policy
-        self.robot = robot
-        self.pinocchio_fk = pinocchio_fk
-        self.motor_kp = motor_kp
-        self.motor_kd = motor_kd
-        self.action_scale = action_scale
-
-        self.obs_dim = policy.get_inputs()[0].shape[1]
-        self.last_action = np.zeros((1, NUM_DOFS), dtype=np.float32)
-        self.motion_command = None
-        self.ref_quat_xyzw = None
-        self.timestep = 0
-        self.yaw_offset = 0.0
-
-        logger.info(f"Dance: obs_dim={self.obs_dim}, action_scale={action_scale}")
-
-    def initialize(self, reset_to_frame_0: bool = True):
-        """Initialize dance. If reset_to_frame_0=True, starts from frame 0. Otherwise resumes."""
-        if reset_to_frame_0:
-            self.timestep = 0
-            self.last_action.fill(0)
-
-            # Get initial motion data at frame 0
-            dummy = np.zeros((1, self.obs_dim), dtype=np.float32)
-            outs = self.policy.run(["joint_pos", "joint_vel", "ref_quat_xyzw"],
-                                  {"obs": dummy, "time_step": np.array([[0]], dtype=np.float32)})
-            self.motion_command = np.concatenate(outs[0:2], axis=1)
-            self.ref_quat_xyzw = outs[2]
-            logger.info("Dance: reset to frame 0")
-        else:
-            # Resume from current timestep - just update motion command for current frame
-            dummy = np.zeros((1, self.obs_dim), dtype=np.float32)
-            outs = self.policy.run(["joint_pos", "joint_vel", "ref_quat_xyzw"],
-                                  {"obs": dummy, "time_step": np.array([[self.timestep]], dtype=np.float32)})
-            self.motion_command = np.concatenate(outs[0:2], axis=1)
-            self.ref_quat_xyzw = outs[2]
-            logger.info(f"Dance: resuming from frame {self.timestep}")
-
-        # Capture yaw offset
-        state = self.robot.lowstate_buffer.get_data()
-        if state and self.pinocchio_fk:
-            quat = np.array(state.imu_state.quaternion, dtype=np.float32)
-            dof = np.array([state.motor_state[i].q for i in range(NUM_DOFS)], dtype=np.float32)
-            torso_q = self.pinocchio_fk.get_torso_quat(np.zeros(3), quat, dof)
-            _, _, self.yaw_offset = quat_to_rpy(torso_q.flatten())
-            logger.info(f"Dance yaw offset: {np.degrees(self.yaw_offset):.1f}°")
-
-    def _remove_yaw_offset(self, quat_wxyz):
-        if abs(self.yaw_offset) < 1e-6:
-            return quat_wxyz
-        yaw_q = rpy_to_quat((0, 0, -self.yaw_offset)).reshape(1, 4)
-        return quat_mul(yaw_q, quat_wxyz)
-
-    def run_step(self):
-        """Single dance step."""
-        state = self.robot.lowstate_buffer.get_data()
-        if state is None:
-            return
-
-        quat = np.array(state.imu_state.quaternion, dtype=np.float32)
-        ang_vel = np.array(state.imu_state.gyroscope, dtype=np.float32)
-        dof_pos = np.array([state.motor_state[i].q for i in range(NUM_DOFS)], dtype=np.float32)
-        dof_vel = np.array([state.motor_state[i].dq for i in range(NUM_DOFS)], dtype=np.float32)
-
-        # FK for torso orientation
-        if self.pinocchio_fk:
-            torso_q = self.pinocchio_fk.get_torso_quat(np.zeros(3), quat, dof_pos)
-            torso_q = self._remove_yaw_offset(torso_q)
-            motion_ori = xyzw_to_wxyz(self.ref_quat_xyzw)
-            rel_quat = subtract_frame_transforms(torso_q, motion_ori)
-            ori_b = matrix_from_quat(rel_quat)[..., :2].reshape(1, -1)
-        else:
-            ori_b = np.zeros((1, 6), dtype=np.float32)
-
-        dof_rel = (dof_pos - DEFAULT_29DOF_ANGLES).reshape(1, -1)
-
-        # Build obs (alphabetical)
-        obs_dict = {
-            "actions": self.last_action,
-            "base_ang_vel": ang_vel.reshape(1, 3),
-            "dof_pos": dof_rel,
-            "dof_vel": dof_vel.reshape(1, -1),
-            "motion_command": self.motion_command,
-            "motion_ref_ori_b": ori_b,
-        }
-        obs = np.concatenate([obs_dict[k].astype(np.float32) for k in sorted(obs_dict.keys())], axis=1)
-        obs = np.clip(obs, -100, 100)
-
-        # Inference
-        outs = self.policy.run(["actions", "joint_pos", "joint_vel", "ref_quat_xyzw"],
-                              {"obs": obs, "time_step": np.array([[self.timestep]], dtype=np.float32)})
-        action = np.clip(outs[0], -100, 100)
-        self.motion_command = np.concatenate(outs[1:3], axis=1)
-        self.ref_quat_xyzw = outs[3]
-        self.last_action = action.copy()
-
-        target = DEFAULT_29DOF_ANGLES + action.flatten() * self.action_scale
-
-        # Send commands
-        for i in range(NUM_DOFS):
-            if i in FROZEN_JOINTS:
-                self.robot.msg.motor_cmd[i].q = 0.0
-                self.robot.msg.motor_cmd[i].kp = FROZEN_KP
-                self.robot.msg.motor_cmd[i].kd = FROZEN_KD
-            else:
-                self.robot.msg.motor_cmd[i].q = float(target[i])
-                self.robot.msg.motor_cmd[i].kp = self.motor_kp[i]
-                self.robot.msg.motor_cmd[i].kd = self.motor_kd[i]
-            self.robot.msg.motor_cmd[i].qd = 0
-            self.robot.msg.motor_cmd[i].tau = 0
-
-        self.robot.send_action(self.robot.msg)
-        self.timestep += 1
-
-
-# =============================================================================
-# MAIN
-# =============================================================================
-
-def main():
-    parser = argparse.ArgumentParser(description="Locomotion ↔ Dance Toggle")
-    parser.add_argument("--loco-repo", type=str, default=DEFAULT_HOLOSOMA_REPO_ID)
-    parser.add_argument("--dance-onnx", type=str, default=DANCE_ONNX_PATH)
-    args = parser.parse_args()
-
-    print("=" * 70)
-    print("🚶 LOCOMOTION ↔ 💃 DANCE")
-    print("=" * 70)
-    print("Press ENTER to toggle between modes")
-    print("=" * 70)
-
-    # Load locomotion policy
-    logger.info("Loading locomotion policy...")
-    loco_path = hf_hub_download(repo_id=args.loco_repo, filename="fastsac_g1_29dof.onnx")
-    loco_policy = ort.InferenceSession(loco_path)
-    loco_obs_dim = loco_policy.get_inputs()[0].shape[1]
-    logger.info(f"Locomotion: {loco_obs_dim}D obs")
-
-    # Load dance policy
-    logger.info("Loading dance policy...")
-    dance_policy = ort.InferenceSession(args.dance_onnx)
-    dance_model = onnx.load(args.dance_onnx)
-    dance_meta = {p.key: json.loads(p.value) for p in dance_model.metadata_props}
-    dance_kp = np.array(dance_meta.get("kp", DEFAULT_29DOF_KP), dtype=np.float32)
-    dance_kd = np.array(dance_meta.get("kd", DEFAULT_29DOF_KD), dtype=np.float32)
-    dance_action_scale = float(dance_meta.get("action_scale", 1.0))
-    logger.info(f"Dance: {dance_policy.get_inputs()[0].shape[1]}D obs, scale={dance_action_scale}")
-
-    # Build Pinocchio FK
-    pinocchio_fk = None
-    if "robot_urdf" in dance_meta:
-        logger.info("Building Pinocchio FK...")
-        pinocchio_fk = PinocchioFK(dance_meta["robot_urdf"])
-
-    # Initialize robot
-    logger.info("Initializing robot...")
-    config = UnitreeG1Config()
-    robot = UnitreeG1(config)
-    logger.info("Robot connected!")
-
-    # Create controllers
-    loco_ctrl = LocomotionController(loco_policy, robot, loco_obs_dim)
-    dance_ctrl = DanceController(dance_policy, robot, pinocchio_fk, dance_kp, dance_kd, dance_action_scale)
-
-    # State
-    mode = "locomotion"
-    toggle_event = threading.Event()
-    shutdown = threading.Event()
-
-    # Input thread
-    def input_loop():
-        while not shutdown.is_set():
-            if select.select([sys.stdin], [], [], 0.1)[0]:
-                sys.stdin.readline()
-                toggle_event.set()
-
-    input_thread = threading.Thread(target=input_loop, daemon=True)
-    input_thread.start()
-
-    print("\n🚶 LOCOMOTION MODE - Use joystick to walk")
-    print("   Press ENTER to switch to DANCE")
-    print("-" * 70)
-
-    step = 0
-    try:
-        while not shutdown.is_set():
-            t0 = time.time()
-
-            # Check toggle
-            if toggle_event.is_set():
-                toggle_event.clear()
-                if mode == "locomotion":
-                    mode = "dance"
-                    dance_ctrl.initialize()
-                    print("\n" + "=" * 70)
-                    print("💃 DANCE MODE (frame 0)")
-                    print("   Press ENTER to switch to LOCOMOTION")
-                    print("=" * 70)
-                else:
-                    mode = "locomotion"
-                    loco_ctrl.reset()
-                    print("\n" + "=" * 70)
-                    print("🚶 LOCOMOTION MODE")
-                    print("   Press ENTER to switch to DANCE")
-                    print("=" * 70)
-
-            # Run controller
-            if mode == "locomotion":
-                loco_ctrl.run_step()
-            else:
-                dance_ctrl.run_step()
-
-            # Log
-            if step % 100 == 0:
-                if mode == "locomotion":
-                    print(f"[LOCO ] step={step:5d} cmd=[{loco_ctrl.cmd[0]:.2f},{loco_ctrl.cmd[1]:.2f},{loco_ctrl.cmd[2]:.2f}]")
-                else:
-                    print(f"[DANCE] step={step:5d} timestep={dance_ctrl.timestep}")
-
-            step += 1
-            elapsed = time.time() - t0
-            if elapsed < CONTROL_DT:
-                time.sleep(CONTROL_DT - elapsed)
-
-    except KeyboardInterrupt:
-        print("\n\nStopping...")
-    finally:
-        shutdown.set()
-        robot.disconnect()
-
-    print("Done!")
-
-
-if __name__ == "__main__":
-    main()

examples/unitree_g1/unitree_rl_locomotion.py

@@ -1,447 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Example: Unitree RL 12-DOF Legs-Only Locomotion (TorchScript)
-
-This example demonstrates loading a 12-DOF legs-only locomotion policy
-(TorchScript .pt format) and running it on the Unitree G1 robot.
-
-Key characteristics:
-- Single TorchScript policy (.pt)
-- 47D observations, 12D actions (legs only)
-- Phase-based gait timing
-- Arms and waist held at fixed positions
-"""
-
-import argparse
-import logging
-import threading
-import time
-
-import numpy as np
-import torch
-from huggingface_hub import hf_hub_download
-from scipy.spatial.transform import Rotation as R
-
-from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
-from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# 12-DOF leg joint configuration
-# Joint order: [L_hip_pitch, L_hip_roll, L_hip_yaw, L_knee, L_ankle_pitch, L_ankle_roll,
-#               R_hip_pitch, R_hip_roll, R_hip_yaw, R_knee, R_ankle_pitch, R_ankle_roll]
-LEG_JOINT_INDICES = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
-
-# Default leg angles for standing
-DEFAULT_LEG_ANGLES = np.array([
-    -0.1, 0.0, 0.0, 0.3, -0.2, 0.0,   # left leg
-    -0.1, 0.0, 0.0, 0.3, -0.2, 0.0,   # right leg
-], dtype=np.float32)
-
-# KP/KD for leg joints
-LEG_KPS = np.array([150, 150, 150, 300, 40, 40, 150, 150, 150, 300, 40, 40], dtype=np.float32)
-LEG_KDS = np.array([6, 6, 6, 4, 2, 2, 6, 6, 6, 4, 2, 2], dtype=np.float32)
-
-# Waist configuration (held at zero)
-WAIST_JOINT_INDICES = [12, 13, 14]  # yaw, roll, pitch
-WAIST_KPS = np.array([250, 250, 250], dtype=np.float32)
-WAIST_KDS = np.array([5, 5, 5], dtype=np.float32)
-
-# Arm configuration (indices 15-28, held at initial position)
-ARM_JOINT_INDICES = list(range(15, 29))
-ARM_KPS = np.array([80, 80, 80, 80, 40, 40, 40,   # left arm (shoulder + wrist)
-                   80, 80, 80, 80, 40, 40, 40], dtype=np.float32)  # right arm
-ARM_KDS = np.array([3, 3, 3, 3, 1.5, 1.5, 1.5,
-                   3, 3, 3, 3, 1.5, 1.5, 1.5], dtype=np.float32)
-
-# Control parameters
-LOCOMOTION_CONTROL_DT = 0.02  # 50Hz control rate
-LOCOMOTION_ACTION_SCALE = 0.25
-ANG_VEL_SCALE = 0.25
-DOF_POS_SCALE = 1.0
-DOF_VEL_SCALE = 0.05
-CMD_SCALE = np.array([2.0, 2.0, 0.25], dtype=np.float32)
-MAX_CMD = np.array([0.8, 0.5, 1.57], dtype=np.float32)  # max vx, vy, yaw_rate
-
-# Gait parameters
-GAIT_PERIOD = 0.8  # seconds
-
-DEFAULT_REPO_ID = "nepyope/unitree_rl_locomotion"
-
-
-def load_torchscript_policy(
-    repo_id: str = DEFAULT_REPO_ID,
-    filename: str = "motion.pt",
-) -> torch.jit.ScriptModule:
-    """Load TorchScript locomotion policy from Hugging Face Hub.
-
-    Args:
-        repo_id: Hugging Face Hub repository ID containing the policy.
-        filename: Policy filename (default: motion.pt).
-    """
-    logger.info(f"Loading TorchScript policy from Hugging Face Hub ({repo_id}/{filename})...")
-
-    policy_path = hf_hub_download(
-        repo_id=repo_id,
-        filename=filename,
-    )
-
-    policy = torch.jit.load(policy_path)
-    policy.eval()
-
-    logger.info("TorchScript policy loaded successfully")
-
-    return policy
-
-
-class UnitreeRLLocomotionController:
-    """
-    Handles 12-DOF legs-only locomotion control for the Unitree G1 robot.
-
-    This controller manages:
-    - Single TorchScript policy
-    - 47D observations (single frame)
-    - 12D action output (legs only)
-    - Arms and waist held at fixed positions
-    - Phase-based gait timing
-    """
-
-    def __init__(self, policy, robot, config):
-        self.policy = policy
-        self.robot = robot
-        self.config = config
-
-        # Velocity commands (vx, vy, yaw_rate)
-        self.locomotion_cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32)
-
-        # State variables (12 DOF legs)
-        self.qj = np.zeros(12, dtype=np.float32)
-        self.dqj = np.zeros(12, dtype=np.float32)
-        self.locomotion_action = np.zeros(12, dtype=np.float32)
-        self.locomotion_obs = np.zeros(47, dtype=np.float32)
-
-        # Initial arm positions (captured on reset)
-        self.initial_arm_positions = np.zeros(14, dtype=np.float32)
-
-        # Counter for phase calculation
-        self.counter = 0
-
-        # Thread management
-        self.locomotion_running = False
-        self.locomotion_thread = None
-
-        logger.info("UnitreeRLLocomotionController initialized")
-        logger.info("  Observation dim: 47, Action dim: 12 (legs only)")
-
-    def locomotion_run(self):
-        """12-DOF legs-only locomotion policy loop."""
-        self.counter += 1
-
-        if self.counter == 1:
-            print("\n" + "=" * 60)
-            print("🚀 RUNNING UNITREE RL 12-DOF LOCOMOTION POLICY")
-            print("   47D observations → 12D actions (legs only)")
-            print("   Arms and waist held at fixed positions")
-            print("=" * 60 + "\n")
-
-        # 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)
-        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 (inverted)
-        self.locomotion_cmd[2] = self.robot.remote_controller.rx * -1  # yaw (inverted)
-
-        # Get leg joint positions and velocities (12 DOF)
-        for i, motor_idx in enumerate(LEG_JOINT_INDICES):
-            self.qj[i] = robot_state.motor_state[motor_idx].q
-            self.dqj[i] = robot_state.motor_state[motor_idx].dq
-
-        # Get IMU data
-        quat = robot_state.imu_state.quaternion
-        ang_vel = np.array(robot_state.imu_state.gyroscope, dtype=np.float32)
-
-        # Scale observations
-        gravity_orientation = self.robot.get_gravity_orientation(quat)
-        qj_obs = (self.qj - DEFAULT_LEG_ANGLES) * DOF_POS_SCALE
-        dqj_obs = self.dqj * DOF_VEL_SCALE
-        ang_vel_scaled = ang_vel * ANG_VEL_SCALE
-
-        # Calculate phase
-        count = self.counter * LOCOMOTION_CONTROL_DT
-        phase = (count % GAIT_PERIOD) / GAIT_PERIOD
-        sin_phase = np.sin(2 * np.pi * phase)
-        cos_phase = np.cos(2 * np.pi * phase)
-
-        # Build 47D observation vector
-        # [0:3]   - angular velocity (scaled)
-        # [3:6]   - gravity orientation
-        # [6:9]   - velocity command (scaled)
-        # [9:21]  - joint positions (12D, relative to default)
-        # [21:33] - joint velocities (12D, scaled)
-        # [33:45] - previous actions (12D)
-        # [45]    - sin_phase
-        # [46]    - cos_phase
-        self.locomotion_obs[0:3] = ang_vel_scaled
-        self.locomotion_obs[3:6] = gravity_orientation
-        self.locomotion_obs[6:9] = self.locomotion_cmd * CMD_SCALE * MAX_CMD
-        self.locomotion_obs[9:21] = qj_obs
-        self.locomotion_obs[21:33] = dqj_obs
-        self.locomotion_obs[33:45] = self.locomotion_action
-        self.locomotion_obs[45] = sin_phase
-        self.locomotion_obs[46] = cos_phase
-
-        # Run policy inference (TorchScript)
-        obs_tensor = torch.from_numpy(self.locomotion_obs).unsqueeze(0).float()
-        with torch.no_grad():
-            action_tensor = self.policy(obs_tensor)
-        self.locomotion_action = action_tensor.squeeze().numpy()
-
-        # Transform action to target joint positions
-        target_leg_pos = DEFAULT_LEG_ANGLES + self.locomotion_action * LOCOMOTION_ACTION_SCALE
-
-        # Debug logging (first 3 iterations)
-        if self.counter <= 3:
-            print(f"\n[Unitree RL Debug #{self.counter}]")
-            print(f"  Phase: {phase:.3f} (sin={sin_phase:.3f}, cos={cos_phase:.3f})")
-            print(f"  Cmd (vx, vy, yaw): ({self.locomotion_cmd[0]:.2f}, {self.locomotion_cmd[1]:.2f}, {self.locomotion_cmd[2]:.2f})")
-            print(f"  Action range: [{self.locomotion_action.min():.3f}, {self.locomotion_action.max():.3f}]")
-
-        # Send commands to LEG motors (0-11)
-        for i, motor_idx in enumerate(LEG_JOINT_INDICES):
-            self.robot.msg.motor_cmd[motor_idx].q = target_leg_pos[i]
-            self.robot.msg.motor_cmd[motor_idx].qd = 0
-            self.robot.msg.motor_cmd[motor_idx].kp = LEG_KPS[i]
-            self.robot.msg.motor_cmd[motor_idx].kd = LEG_KDS[i]
-            self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-        # Hold WAIST motors at zero (12, 13, 14)
-        for i, motor_idx in enumerate(WAIST_JOINT_INDICES):
-            self.robot.msg.motor_cmd[motor_idx].q = 0.0
-            self.robot.msg.motor_cmd[motor_idx].qd = 0
-            self.robot.msg.motor_cmd[motor_idx].kp = WAIST_KPS[i]
-            self.robot.msg.motor_cmd[motor_idx].kd = WAIST_KDS[i]
-            self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-        # Hold ARM motors at initial position (15-28)
-        for i, motor_idx in enumerate(ARM_JOINT_INDICES):
-            self.robot.msg.motor_cmd[motor_idx].q = self.initial_arm_positions[i]
-            self.robot.msg.motor_cmd[motor_idx].qd = 0
-            self.robot.msg.motor_cmd[motor_idx].kp = ARM_KPS[i]
-            self.robot.msg.motor_cmd[motor_idx].kd = ARM_KDS[i]
-            self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-        # Send command
-        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.locomotion_run()
-            except Exception as e:
-                logger.error(f"Error in locomotion loop: {e}")
-                import traceback
-                traceback.print_exc()
-
-            # 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 legs to default standing position over 2 seconds (arms are captured and held)."""
-        logger.info("Moving legs to default position...")
-
-        total_time = 2.0
-        num_step = int(total_time / self.robot.control_dt)
-
-        # Get current state
-        robot_state = self.robot.get_observation()
-
-        # Capture initial arm positions (to hold during locomotion)
-        for i, motor_idx in enumerate(ARM_JOINT_INDICES):
-            self.initial_arm_positions[i] = robot_state.motor_state[motor_idx].q
-        logger.info(f"Captured initial arm positions: {self.initial_arm_positions[:4]}...")
-
-        # Record current leg positions
-        init_leg_pos = np.zeros(12, dtype=np.float32)
-        for i, motor_idx in enumerate(LEG_JOINT_INDICES):
-            init_leg_pos[i] = robot_state.motor_state[motor_idx].q
-
-        # Interpolate legs to default position
-        for step in range(num_step):
-            alpha = step / num_step
-
-            # Interpolate leg positions
-            for i, motor_idx in enumerate(LEG_JOINT_INDICES):
-                target_pos = DEFAULT_LEG_ANGLES[i]
-                self.robot.msg.motor_cmd[motor_idx].q = (
-                    init_leg_pos[i] * (1 - alpha) + target_pos * alpha
-                )
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = LEG_KPS[i]
-                self.robot.msg.motor_cmd[motor_idx].kd = LEG_KDS[i]
-                self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-            # Hold waist at zero
-            for i, motor_idx in enumerate(WAIST_JOINT_INDICES):
-                self.robot.msg.motor_cmd[motor_idx].q = 0.0
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = WAIST_KPS[i]
-                self.robot.msg.motor_cmd[motor_idx].kd = WAIST_KDS[i]
-                self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-            # Hold arms at initial position
-            for i, motor_idx in enumerate(ARM_JOINT_INDICES):
-                self.robot.msg.motor_cmd[motor_idx].q = self.initial_arm_positions[i]
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = ARM_KPS[i]
-                self.robot.msg.motor_cmd[motor_idx].kd = ARM_KDS[i]
-                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 leg position")
-
-        # Hold position for 2 seconds
-        logger.info("Holding default position for 2 seconds...")
-        hold_time = 2.0
-        num_hold_steps = int(hold_time / self.robot.control_dt)
-
-        for _ in range(num_hold_steps):
-            # Hold legs at default
-            for i, motor_idx in enumerate(LEG_JOINT_INDICES):
-                self.robot.msg.motor_cmd[motor_idx].q = DEFAULT_LEG_ANGLES[i]
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = LEG_KPS[i]
-                self.robot.msg.motor_cmd[motor_idx].kd = LEG_KDS[i]
-                self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-            # Hold waist at zero
-            for i, motor_idx in enumerate(WAIST_JOINT_INDICES):
-                self.robot.msg.motor_cmd[motor_idx].q = 0.0
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = WAIST_KPS[i]
-                self.robot.msg.motor_cmd[motor_idx].kd = WAIST_KDS[i]
-                self.robot.msg.motor_cmd[motor_idx].tau = 0
-
-            # Hold arms at initial position
-            for i, motor_idx in enumerate(ARM_JOINT_INDICES):
-                self.robot.msg.motor_cmd[motor_idx].q = self.initial_arm_positions[i]
-                self.robot.msg.motor_cmd[motor_idx].qd = 0
-                self.robot.msg.motor_cmd[motor_idx].kp = ARM_KPS[i]
-                self.robot.msg.motor_cmd[motor_idx].kd = ARM_KDS[i]
-                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("Ready to start locomotion!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Unitree RL 12-DOF Locomotion Controller for Unitree G1")
-    parser.add_argument(
-        "--repo-id",
-        type=str,
-        default=DEFAULT_REPO_ID,
-        help=f"Hugging Face Hub repo ID for policy (default: {DEFAULT_REPO_ID})",
-    )
-    parser.add_argument(
-        "--filename",
-        type=str,
-        default="motion.pt",
-        help="Policy filename (default: motion.pt)",
-    )
-    args = parser.parse_args()
-
-    # Load policy
-    policy = load_torchscript_policy(repo_id=args.repo_id, filename=args.filename)
-
-    # Initialize robot
-    config = UnitreeG1Config()
-    robot = UnitreeG1(config)
-
-    # Initialize locomotion controller
-    locomotion_controller = UnitreeRLLocomotionController(
-        policy=policy,
-        robot=robot,
-        config=config,
-    )
-
-    # Reset robot and start locomotion thread
-    try:
-        locomotion_controller.reset_robot()
-        locomotion_controller.start_locomotion_thread()
-
-        # Log status
-        logger.info("Robot initialized with Unitree RL locomotion policy")
-        logger.info("Locomotion controller running in background thread")
-        logger.info("Use remote controller to command velocity:")
-        logger.info("  Left stick Y: forward/backward")
-        logger.info("  Left stick X: left/right")
-        logger.info("  Right stick X: rotate")
-        logger.info("Press Ctrl+C to stop")
-
-        # Keep robot alive
-        while True:
-            time.sleep(1.0)
-    except KeyboardInterrupt:
-        print("\nStopping locomotion...")
-        locomotion_controller.stop_locomotion_thread()
-        print("Done!")
-

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.4.3"
+version = "0.4.1"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 readme = "README.md"
 license = { text = "Apache-2.0" }
@@ -102,15 +102,13 @@ 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 = [
@@ -133,7 +131,6 @@ groot = [
     "ninja>=1.11.1,<2.0.0",
     "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
-xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 
 # Features
@@ -147,12 +144,13 @@ video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 # Simulation
 aloha = ["gym-aloha>=0.1.2,<0.2.0"]
 pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
-libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0"]
+libero = ["lerobot[transformers-dep]", "libero @ git+https://github.com/huggingface/lerobot-libero.git@main#egg=libero"]
 metaworld = ["metaworld==3.0.0"]
 
 # All
 all = [
     "lerobot[dynamixel]",
+    "lerobot[openarms]",
     "lerobot[gamepad]",
     "lerobot[hopejr]",
     "lerobot[lekiwi]",
@@ -162,7 +160,6 @@ all = [
     "lerobot[pi]",
     "lerobot[smolvla]",
     # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
-    "lerobot[xvla]",
     "lerobot[hilserl]",
     "lerobot[async]",
     "lerobot[dev]",
@@ -263,7 +260,6 @@ default.extend-ignore-identifiers-re = [
     "ein",
     "thw",
     "inpt",
-    "ROBOTIS",
 ]
 
 # TODO: Uncomment when ready to use
@@ -363,9 +359,9 @@ 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", "omx_follower"]
+SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so100_follower"]

src/lerobot/async_inference/robot_client.py

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

src/lerobot/configs/types.py

@@ -43,10 +43,3 @@ class NormalizationMode(str, Enum):
 class PolicyFeature:
     type: FeatureType
     shape: tuple[int, ...]
-
-
-class RTCAttentionSchedule(str, Enum):
-    ZEROS = "ZEROS"
-    ONES = "ONES"
-    LINEAR = "LINEAR"
-    EXP = "EXP"

src/lerobot/datasets/aggregate.py

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

src/lerobot/datasets/dataset_tools.py

@@ -39,7 +39,6 @@ from lerobot.datasets.aggregate import aggregate_datasets
 from lerobot.datasets.compute_stats import aggregate_stats
 from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.datasets.utils import (
-    DATA_DIR,
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_DATA_PATH,
@@ -963,23 +962,28 @@ def _copy_data_with_feature_changes(
     remove_features: list[str] | None = None,
 ) -> None:
     """Copy data while adding or removing features."""
-    data_dir = dataset.root / DATA_DIR
-    parquet_files = sorted(data_dir.glob("*/*.parquet"))
+    if dataset.meta.episodes is None:
+        dataset.meta.episodes = load_episodes(dataset.meta.root)
 
-    if not parquet_files:
-        raise ValueError(f"No parquet files found in {data_dir}")
+    # Map file paths to episode indices to extract chunk/file indices
+    file_to_episodes: dict[Path, set[int]] = {}
+    for ep_idx in range(dataset.meta.total_episodes):
+        file_path = dataset.meta.get_data_file_path(ep_idx)
+        if file_path not in file_to_episodes:
+            file_to_episodes[file_path] = set()
+        file_to_episodes[file_path].add(ep_idx)
 
     frame_idx = 0
 
-    for src_path in tqdm(parquet_files, desc="Processing data files"):
-        df = pd.read_parquet(src_path).reset_index(drop=True)
+    for src_path in tqdm(sorted(file_to_episodes.keys()), desc="Processing data files"):
+        df = pd.read_parquet(dataset.root / src_path).reset_index(drop=True)
 
-        relative_path = src_path.relative_to(dataset.root)
-        chunk_dir = relative_path.parts[1]
-        file_name = relative_path.parts[2]
-
-        chunk_idx = int(chunk_dir.split("-")[1])
-        file_idx = int(file_name.split("-")[1].split(".")[0])
+        # Get chunk_idx and file_idx from the source file's first episode
+        episodes_in_file = file_to_episodes[src_path]
+        first_ep_idx = min(episodes_in_file)
+        src_ep = dataset.meta.episodes[first_ep_idx]
+        chunk_idx = src_ep["data/chunk_index"]
+        file_idx = src_ep["data/file_index"]
 
         if remove_features:
             df = df.drop(columns=remove_features, errors="ignore")
@@ -1005,7 +1009,7 @@ def _copy_data_with_feature_changes(
                         df[feature_name] = feature_slice
             frame_idx = end_idx
 
-        # Write using the same chunk/file structure as source
+        # Write using the preserved chunk_idx and file_idx from source
         dst_path = new_meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
         dst_path.parent.mkdir(parents=True, exist_ok=True)
 

src/lerobot/datasets/image_writer.py

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

src/lerobot/datasets/lerobot_dataset.py

@@ -13,7 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import concurrent.futures
 import contextlib
 import logging
 import shutil
@@ -431,7 +430,9 @@ class LeRobotDatasetMetadata:
         video_keys = [video_key] if video_key is not None else self.video_keys
         for key in video_keys:
             if not self.features[key].get("info", None):
-                video_path = self.root / self.video_path.format(video_key=key, chunk_index=0, file_index=0)
+                video_path = self.root / self.video_path.format(
+                    video_key=video_key, chunk_index=0, file_index=0
+                )
                 self.info["features"][key]["info"] = get_video_info(video_path)
 
     def update_chunk_settings(
@@ -540,15 +541,6 @@ class LeRobotDatasetMetadata:
         return obj
 
 
-def _encode_video_worker(video_key: str, episode_index: int, root: Path, fps: int) -> Path:
-    temp_path = Path(tempfile.mkdtemp(dir=root)) / f"{video_key}_{episode_index:03d}.mp4"
-    fpath = DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=episode_index, frame_index=0)
-    img_dir = (root / fpath).parent
-    encode_video_frames(img_dir, temp_path, fps, overwrite=True)
-    shutil.rmtree(img_dir)
-    return temp_path
-
-
 class LeRobotDataset(torch.utils.data.Dataset):
     def __init__(
         self,
@@ -722,15 +714,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             self.download(download_videos)
             self.hf_dataset = self.load_hf_dataset()
 
-        # Create mapping from absolute indices to relative indices when only a subset of the episodes are loaded
-        # Build a mapping: absolute_index -> relative_index_in_filtered_dataset
-        self._absolute_to_relative_idx = None
-        if self.episodes is not None:
-            self._absolute_to_relative_idx = {
-                abs_idx.item() if isinstance(abs_idx, torch.Tensor) else abs_idx: rel_idx
-                for rel_idx, abs_idx in enumerate(self.hf_dataset["index"])
-            }
-
         # Setup delta_indices
         if self.delta_timestamps is not None:
             check_delta_timestamps(self.delta_timestamps, self.fps, self.tolerance_s)
@@ -849,7 +832,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def load_hf_dataset(self) -> datasets.Dataset:
         """hf_dataset contains all the observations, states, actions, rewards, etc."""
         features = get_hf_features_from_features(self.features)
-        hf_dataset = load_nested_dataset(self.root / "data", features=features, episodes=self.episodes)
+        hf_dataset = load_nested_dataset(self.root / "data", features=features)
         hf_dataset.set_transform(hf_transform_to_torch)
         return hf_dataset
 
@@ -866,8 +849,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         # Determine requested episodes
         if self.episodes is None:
+            # Requesting all episodes - check if we have all episodes from metadata
             requested_episodes = set(range(self.meta.total_episodes))
         else:
+            # Requesting specific episodes
             requested_episodes = set(self.episodes)
 
         # Check if all requested episodes are available in cached data
@@ -949,11 +934,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         query_timestamps = {}
         for key in self.meta.video_keys:
             if query_indices is not None and key in query_indices:
-                if self._absolute_to_relative_idx is not None:
-                    relative_indices = [self._absolute_to_relative_idx[idx] for idx in query_indices[key]]
-                    timestamps = self.hf_dataset[relative_indices]["timestamp"]
-                else:
-                    timestamps = self.hf_dataset[query_indices[key]]["timestamp"]
+                timestamps = self.hf_dataset[query_indices[key]]["timestamp"]
                 query_timestamps[key] = torch.stack(timestamps).tolist()
             else:
                 query_timestamps[key] = [current_ts]
@@ -961,32 +942,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return query_timestamps
 
     def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
-        """
-        Query dataset for indices across keys, skipping video keys.
-
-        Tries column-first [key][indices] for speed, falls back to row-first.
-
-        Args:
-            query_indices: Dict mapping keys to index lists to retrieve
-
-        Returns:
-            Dict with stacked tensors of queried data (video keys excluded)
-        """
-        result: dict = {}
-        for key, q_idx in query_indices.items():
-            if key in self.meta.video_keys:
-                continue
-            # Map absolute indices to relative indices if needed
-            relative_indices = (
-                q_idx
-                if self._absolute_to_relative_idx is None
-                else [self._absolute_to_relative_idx[idx] for idx in q_idx]
-            )
-            try:
-                result[key] = torch.stack(self.hf_dataset[key][relative_indices])
-            except (KeyError, TypeError, IndexError):
-                result[key] = torch.stack(self.hf_dataset[relative_indices][key])
-        return result
+        return {
+            key: torch.stack(self.hf_dataset[q_idx][key])
+            for key, q_idx in query_indices.items()
+            if key not in self.meta.video_keys
+        }
 
     def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
         """Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function
@@ -1081,7 +1041,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             ep_buffer[key] = current_ep_idx if key == "episode_index" else []
         return ep_buffer
 
-    # TODO(Steven): consider move this to utils
     def _get_image_file_path(self, episode_index: int, image_key: str, frame_index: int) -> Path:
         fpath = DEFAULT_IMAGE_PATH.format(
             image_key=image_key, episode_index=episode_index, frame_index=frame_index
@@ -1091,15 +1050,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def _get_image_file_dir(self, episode_index: int, image_key: str) -> Path:
         return self._get_image_file_path(episode_index, image_key, frame_index=0).parent
 
-    def _save_image(
-        self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1
-    ) -> None:
+    def _save_image(self, image: torch.Tensor | np.ndarray | PIL.Image.Image, fpath: Path) -> None:
         if self.image_writer is None:
             if isinstance(image, torch.Tensor):
                 image = image.cpu().numpy()
-            write_image(image, fpath, compress_level=compress_level)
+            write_image(image, fpath)
         else:
-            self.image_writer.save_image(image=image, fpath=fpath, compress_level=compress_level)
+            self.image_writer.save_image(image=image, fpath=fpath)
 
     def add_frame(self, frame: dict) -> None:
         """
@@ -1137,19 +1094,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 )
                 if frame_index == 0:
                     img_path.parent.mkdir(parents=True, exist_ok=True)
-                compress_level = 1 if self.features[key]["dtype"] == "video" else 6
-                self._save_image(frame[key], img_path, compress_level)
+                self._save_image(frame[key], img_path)
                 self.episode_buffer[key].append(str(img_path))
             else:
                 self.episode_buffer[key].append(frame[key])
 
         self.episode_buffer["size"] += 1
 
-    def save_episode(
-        self,
-        episode_data: dict | None = None,
-        parallel_encoding: bool = True,
-    ) -> None:
+    def save_episode(self, episode_data: dict | None = None) -> None:
         """
         This will save to disk the current episode in self.episode_buffer.
 
@@ -1161,8 +1113,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             episode_data (dict | None, optional): Dict containing the episode data to save. If None, this will
                 save the current episode in self.episode_buffer, which is filled with 'add_frame'. Defaults to
                 None.
-            parallel_encoding (bool, optional): If True, encode videos in parallel using ProcessPoolExecutor.
-                Defaults to True on Linux, False on macOS as it tends to use all the CPU available already.
         """
         episode_buffer = episode_data if episode_data is not None else self.episode_buffer
 
@@ -1199,40 +1149,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         use_batched_encoding = self.batch_encoding_size > 1
 
         if has_video_keys and not use_batched_encoding:
-            num_cameras = len(self.meta.video_keys)
-            if parallel_encoding and num_cameras > 1:
-                # TODO(Steven): Ideally we would like to control the number of threads per encoding such that:
-                # num_cameras * num_threads = (total_cpu -1)
-                with concurrent.futures.ProcessPoolExecutor(max_workers=num_cameras) as executor:
-                    future_to_key = {
-                        executor.submit(
-                            _encode_video_worker,
-                            video_key,
-                            episode_index,
-                            self.root,
-                            self.fps,
-                        ): video_key
-                        for video_key in self.meta.video_keys
-                    }
-
-                    results = {}
-                    for future in concurrent.futures.as_completed(future_to_key):
-                        video_key = future_to_key[future]
-                        try:
-                            temp_path = future.result()
-                            results[video_key] = temp_path
-                        except Exception as exc:
-                            logging.error(f"Video encoding failed for {video_key}: {exc}")
-                            raise exc
-
-                for video_key in self.meta.video_keys:
-                    temp_path = results[video_key]
-                    ep_metadata.update(
-                        self._save_episode_video(video_key, episode_index, temp_path=temp_path)
-                    )
-            else:
-                for video_key in self.meta.video_keys:
-                    ep_metadata.update(self._save_episode_video(video_key, episode_index))
+            for video_key in self.meta.video_keys:
+                ep_metadata.update(self._save_episode_video(video_key, episode_index))
 
         # `meta.save_episode` need to be executed after encoding the videos
         self.meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats, ep_metadata)
@@ -1397,18 +1315,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         return metadata
 
-    def _save_episode_video(
-        self,
-        video_key: str,
-        episode_index: int,
-        temp_path: Path | None = None,
-    ) -> dict:
+    def _save_episode_video(self, video_key: str, episode_index: int) -> dict:
         # Encode episode frames into a temporary video
-        if temp_path is None:
-            ep_path = self._encode_temporary_episode_video(video_key, episode_index)
-        else:
-            ep_path = temp_path
-
+        ep_path = self._encode_temporary_episode_video(video_key, episode_index)
         ep_size_in_mb = get_file_size_in_mb(ep_path)
         ep_duration_in_s = get_video_duration_in_s(ep_path)
 
@@ -1526,7 +1435,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
         since video encoding with ffmpeg is already using multithreading.
         """
-        return _encode_video_worker(video_key, episode_index, self.root, self.fps)
+        temp_path = Path(tempfile.mkdtemp(dir=self.root)) / f"{video_key}_{episode_index:03d}.mp4"
+        img_dir = self._get_image_file_dir(episode_index, video_key)
+        encode_video_frames(img_dir, temp_path, self.fps, overwrite=True)
+        shutil.rmtree(img_dir)
+        return temp_path
 
     @classmethod
     def create(
@@ -1572,7 +1485,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.image_transforms = None
         obj.delta_timestamps = None
         obj.delta_indices = None
-        obj._absolute_to_relative_idx = None
         obj.video_backend = video_backend if video_backend is not None else get_safe_default_codec()
         obj.writer = None
         obj.latest_episode = None

src/lerobot/datasets/utils.py

@@ -28,7 +28,6 @@ import numpy as np
 import packaging.version
 import pandas
 import pandas as pd
-import pyarrow.dataset as pa_ds
 import pyarrow.parquet as pq
 import torch
 from datasets import Dataset
@@ -49,7 +48,7 @@ from lerobot.utils.utils import SuppressProgressBars, is_valid_numpy_dtype_strin
 
 DEFAULT_CHUNK_SIZE = 1000  # Max number of files per chunk
 DEFAULT_DATA_FILE_SIZE_IN_MB = 100  # Max size per file
-DEFAULT_VIDEO_FILE_SIZE_IN_MB = 200  # Max size per file
+DEFAULT_VIDEO_FILE_SIZE_IN_MB = 500  # Max size per file
 
 INFO_PATH = "meta/info.json"
 STATS_PATH = "meta/stats.json"
@@ -104,9 +103,7 @@ def update_chunk_file_indices(chunk_idx: int, file_idx: int, chunks_size: int) -
     return chunk_idx, file_idx
 
 
-def load_nested_dataset(
-    pq_dir: Path, features: datasets.Features | None = None, episodes: list[int] | None = None
-) -> Dataset:
+def load_nested_dataset(pq_dir: Path, features: datasets.Features | None = None) -> Dataset:
     """Find parquet files in provided directory {pq_dir}/chunk-xxx/file-xxx.parquet
     Convert parquet files to pyarrow memory mapped in a cache folder for efficient RAM usage
     Concatenate all pyarrow references to return HF Dataset format
@@ -114,26 +111,15 @@ def load_nested_dataset(
     Args:
         pq_dir: Directory containing parquet files
         features: Optional features schema to ensure consistent loading of complex types like images
-        episodes: Optional list of episode indices to filter. Uses PyArrow predicate pushdown for efficiency.
     """
     paths = sorted(pq_dir.glob("*/*.parquet"))
     if len(paths) == 0:
         raise FileNotFoundError(f"Provided directory does not contain any parquet file: {pq_dir}")
 
+    # TODO(rcadene): set num_proc to accelerate conversion to pyarrow
     with SuppressProgressBars():
-        # When no filtering needed, Dataset uses memory-mapped loading for efficiency
-        # PyArrow loads the entire dataset into memory
-        if episodes is None:
-            return Dataset.from_parquet([str(path) for path in paths], features=features)
-
-        arrow_dataset = pa_ds.dataset(paths, format="parquet")
-        filter_expr = pa_ds.field("episode_index").isin(episodes)
-        table = arrow_dataset.to_table(filter=filter_expr)
-
-        if features is not None:
-            table = table.cast(features.arrow_schema)
-
-        return Dataset(table)
+        datasets = Dataset.from_parquet([str(path) for path in paths], features=features)
+    return datasets
 
 
 def get_parquet_num_frames(parquet_path: str | Path) -> int:

src/lerobot/datasets/video_utils.py

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

src/lerobot/envs/configs.py

@@ -21,22 +21,7 @@ import draccus
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.robots import RobotConfig
 from lerobot.teleoperators.config import TeleoperatorConfig
-from lerobot.utils.constants import (
-    ACTION,
-    LIBERO_KEY_EEF_MAT,
-    LIBERO_KEY_EEF_POS,
-    LIBERO_KEY_EEF_QUAT,
-    LIBERO_KEY_GRIPPER_QPOS,
-    LIBERO_KEY_GRIPPER_QVEL,
-    LIBERO_KEY_JOINTS_POS,
-    LIBERO_KEY_JOINTS_VEL,
-    LIBERO_KEY_PIXELS_AGENTVIEW,
-    LIBERO_KEY_PIXELS_EYE_IN_HAND,
-    OBS_ENV_STATE,
-    OBS_IMAGE,
-    OBS_IMAGES,
-    OBS_STATE,
-)
+from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
 
 
 @dataclass
@@ -245,7 +230,7 @@ class HILSerlRobotEnvConfig(EnvConfig):
 class LiberoEnv(EnvConfig):
     task: str = "libero_10"  # can also choose libero_spatial, libero_object, etc.
     fps: int = 30
-    episode_length: int | None = None
+    episode_length: int = 520
     obs_type: str = "pixels_agent_pos"
     render_mode: str = "rgb_array"
     camera_name: str = "agentview_image,robot0_eye_in_hand_image"
@@ -261,62 +246,28 @@ class LiberoEnv(EnvConfig):
     features_map: dict[str, str] = field(
         default_factory=lambda: {
             ACTION: ACTION,
-            LIBERO_KEY_EEF_POS: f"{OBS_STATE}.eef_pos",
-            LIBERO_KEY_EEF_QUAT: f"{OBS_STATE}.eef_quat",
-            LIBERO_KEY_EEF_MAT: f"{OBS_STATE}.eef_mat",
-            LIBERO_KEY_GRIPPER_QPOS: f"{OBS_STATE}.gripper_qpos",
-            LIBERO_KEY_GRIPPER_QVEL: f"{OBS_STATE}.gripper_qvel",
-            LIBERO_KEY_JOINTS_POS: f"{OBS_STATE}.joint_pos",
-            LIBERO_KEY_JOINTS_VEL: f"{OBS_STATE}.joint_vel",
-            LIBERO_KEY_PIXELS_AGENTVIEW: f"{OBS_IMAGES}.image",
-            LIBERO_KEY_PIXELS_EYE_IN_HAND: f"{OBS_IMAGES}.image2",
+            "agent_pos": OBS_STATE,
+            "pixels/agentview_image": f"{OBS_IMAGES}.image",
+            "pixels/robot0_eye_in_hand_image": f"{OBS_IMAGES}.image2",
         }
     )
-    control_mode: str = "relative"  # or "absolute"
 
     def __post_init__(self):
         if self.obs_type == "pixels":
-            self.features[LIBERO_KEY_PIXELS_AGENTVIEW] = PolicyFeature(
+            self.features["pixels/agentview_image"] = PolicyFeature(
                 type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3)
             )
-            self.features[LIBERO_KEY_PIXELS_EYE_IN_HAND] = PolicyFeature(
+            self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature(
                 type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3)
             )
         elif self.obs_type == "pixels_agent_pos":
-            self.features[LIBERO_KEY_PIXELS_AGENTVIEW] = PolicyFeature(
+            self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(8,))
+            self.features["pixels/agentview_image"] = PolicyFeature(
                 type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3)
             )
-            self.features[LIBERO_KEY_PIXELS_EYE_IN_HAND] = PolicyFeature(
+            self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature(
                 type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3)
             )
-            self.features[LIBERO_KEY_EEF_POS] = PolicyFeature(
-                type=FeatureType.STATE,
-                shape=(3,),
-            )
-            self.features[LIBERO_KEY_EEF_QUAT] = PolicyFeature(
-                type=FeatureType.STATE,
-                shape=(4,),
-            )
-            self.features[LIBERO_KEY_EEF_MAT] = PolicyFeature(
-                type=FeatureType.STATE,
-                shape=(3, 3),
-            )
-            self.features[LIBERO_KEY_GRIPPER_QPOS] = PolicyFeature(
-                type=FeatureType.STATE,
-                shape=(2,),
-            )
-            self.features[LIBERO_KEY_GRIPPER_QVEL] = PolicyFeature(
-                type=FeatureType.STATE,
-                shape=(2,),
-            )
-            self.features[LIBERO_KEY_JOINTS_POS] = PolicyFeature(
-                type=FeatureType.STATE,
-                shape=(7,),
-            )
-            self.features[LIBERO_KEY_JOINTS_VEL] = PolicyFeature(
-                type=FeatureType.STATE,
-                shape=(7,),
-            )
         else:
             raise ValueError(f"Unsupported obs_type: {self.obs_type}")
 

src/lerobot/envs/factory.py

@@ -14,18 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import importlib
-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
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:
@@ -39,65 +32,16 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
         raise ValueError(f"Policy type '{env_type}' is not available.")
 
 
-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]],
-]:
-    """
-    Create preprocessor and postprocessor pipelines for environment observations.
-
-    This function creates processor pipelines that transform raw environment
-    observations and actions. By default, it returns identity processors that do nothing.
-    For specific environments like LIBERO, it adds environment-specific processing steps.
-
-    Args:
-        env_cfg: The configuration of the environment.
-
-    Returns:
-        A tuple containing:
-            - preprocessor: Pipeline that processes environment observations
-            - postprocessor: Pipeline that processes environment outputs (currently identity)
-    """
-    # 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:
-        preprocessor_steps.append(LiberoProcessorStep())
-
-    preprocessor = PolicyProcessorPipeline(steps=preprocessor_steps)
-    postprocessor = PolicyProcessorPipeline(steps=postprocessor_steps)
-
-    return preprocessor, postprocessor
-
-
 def make_env(
-    cfg: EnvConfig | str,
-    n_envs: int = 1,
-    use_async_envs: bool = False,
-    hub_cache_dir: str | None = None,
-    trust_remote_code: bool = False,
+    cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False
 ) -> dict[str, dict[int, gym.vector.VectorEnv]]:
-    """Makes a gym vector environment according to the config or Hub reference.
+    """Makes a gym vector environment according to the config.
 
     Args:
-        cfg (EnvConfig | str): Either an `EnvConfig` object describing the environment to build locally,
-            or a Hugging Face Hub repository identifier (e.g. `"username/repo"`). In the latter case,
-            the repo must include a Python file (usually `env.py`).
+        cfg (EnvConfig): the config of the environment to instantiate.
         n_envs (int, optional): The number of parallelized env to return. Defaults to 1.
         use_async_envs (bool, optional): Whether to return an AsyncVectorEnv or a SyncVectorEnv. Defaults to
             False.
-        hub_cache_dir (str | None): Optional cache path for downloaded hub files.
-        trust_remote_code (bool): **Explicit consent** to execute remote code from the Hub.
-            Default False — must be set to True to import/exec hub `env.py`.
 
     Raises:
         ValueError: if n_envs < 1
@@ -110,21 +54,6 @@ def make_env(
             - For single-task environments: a single suite entry (cfg.type) with task_id=0.
 
     """
-    # if user passed a hub id string (e.g., "username/repo", "username/repo@main:env.py")
-    # simplified: only support hub-provided `make_env`
-    if isinstance(cfg, str):
-        # _download_hub_file will raise the same RuntimeError if trust_remote_code is False
-        repo_id, file_path, local_file, revision = _download_hub_file(cfg, trust_remote_code, hub_cache_dir)
-
-        # import and surface clear import errors
-        module = _import_hub_module(local_file, repo_id)
-
-        # call the hub-provided make_env
-        raw_result = _call_make_env(module, n_envs=n_envs, use_async_envs=use_async_envs)
-
-        # normalize the return into {suite: {task_id: vec_env}}
-        return _normalize_hub_result(raw_result)
-
     if n_envs < 1:
         raise ValueError("`n_envs` must be at least 1")
 
@@ -143,8 +72,6 @@ def make_env(
             init_states=cfg.init_states,
             gym_kwargs=cfg.gym_kwargs,
             env_cls=env_cls,
-            control_mode=cfg.control_mode,
-            episode_length=cfg.episode_length,
         )
     elif "metaworld" in cfg.type:
         from lerobot.envs.metaworld import create_metaworld_envs

src/lerobot/envs/libero.py

@@ -28,6 +28,7 @@ import torch
 from gymnasium import spaces
 from libero.libero import benchmark, get_libero_path
 from libero.libero.envs import OffScreenRenderEnv
+from robosuite.utils.transform_utils import quat2axisangle
 
 
 def _parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
@@ -80,7 +81,10 @@ def get_libero_dummy_action():
     return [0, 0, 0, 0, 0, 0, -1]
 
 
+OBS_STATE_DIM = 8
 ACTION_DIM = 7
+AGENT_POS_LOW = -1000.0
+AGENT_POS_HIGH = 1000.0
 ACTION_LOW = -1.0
 ACTION_HIGH = 1.0
 TASK_SUITE_MAX_STEPS: dict[str, int] = {
@@ -100,7 +104,6 @@ class LiberoEnv(gym.Env):
         task_suite: Any,
         task_id: int,
         task_suite_name: str,
-        episode_length: int | None = None,
         camera_name: str | Sequence[str] = "agentview_image,robot0_eye_in_hand_image",
         obs_type: str = "pixels",
         render_mode: str = "rgb_array",
@@ -112,7 +115,6 @@ class LiberoEnv(gym.Env):
         episode_index: int = 0,
         camera_name_mapping: dict[str, str] | None = None,
         num_steps_wait: int = 10,
-        control_mode: str = "relative",
     ):
         super().__init__()
         self.task_id = task_id
@@ -140,19 +142,14 @@ class LiberoEnv(gym.Env):
         self.camera_name_mapping = camera_name_mapping
         self.num_steps_wait = num_steps_wait
         self.episode_index = episode_index
-        self.episode_length = episode_length
         # Load once and keep
         self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None
         self._init_state_id = self.episode_index  # tie each sub-env to a fixed init state
 
         self._env = self._make_envs_task(task_suite, self.task_id)
         default_steps = 500
-        self._max_episode_steps = (
-            TASK_SUITE_MAX_STEPS.get(task_suite_name, default_steps)
-            if self.episode_length is None
-            else self.episode_length
-        )
-        self.control_mode = control_mode
+        self._max_episode_steps = TASK_SUITE_MAX_STEPS.get(task_suite_name, default_steps)
+
         images = {}
         for cam in self.camera_name:
             images[self.camera_name_mapping[cam]] = spaces.Box(
@@ -178,36 +175,11 @@ class LiberoEnv(gym.Env):
             self.observation_space = spaces.Dict(
                 {
                     "pixels": spaces.Dict(images),
-                    "robot_state": spaces.Dict(
-                        {
-                            "eef": spaces.Dict(
-                                {
-                                    "pos": spaces.Box(low=-np.inf, high=np.inf, shape=(3,), dtype=np.float64),
-                                    "quat": spaces.Box(
-                                        low=-np.inf, high=np.inf, shape=(4,), dtype=np.float64
-                                    ),
-                                    "mat": spaces.Box(
-                                        low=-np.inf, high=np.inf, shape=(3, 3), dtype=np.float64
-                                    ),
-                                }
-                            ),
-                            "gripper": spaces.Dict(
-                                {
-                                    "qpos": spaces.Box(
-                                        low=-np.inf, high=np.inf, shape=(2,), dtype=np.float64
-                                    ),
-                                    "qvel": spaces.Box(
-                                        low=-np.inf, high=np.inf, shape=(2,), dtype=np.float64
-                                    ),
-                                }
-                            ),
-                            "joints": spaces.Dict(
-                                {
-                                    "pos": spaces.Box(low=-np.inf, high=np.inf, shape=(7,), dtype=np.float64),
-                                    "vel": spaces.Box(low=-np.inf, high=np.inf, shape=(7,), dtype=np.float64),
-                                }
-                            ),
-                        }
+                    "agent_pos": spaces.Box(
+                        low=AGENT_POS_LOW,
+                        high=AGENT_POS_HIGH,
+                        shape=(OBS_STATE_DIM,),
+                        dtype=np.float64,
                     ),
                 }
             )
@@ -219,7 +191,6 @@ class LiberoEnv(gym.Env):
     def render(self):
         raw_obs = self._env.env._get_observations()
         image = self._format_raw_obs(raw_obs)["pixels"]["image"]
-        image = image[::-1, ::-1]  # flip both H and W for visualization
         return image
 
     def _make_envs_task(self, task_suite: Any, task_id: int = 0):
@@ -241,48 +212,23 @@ class LiberoEnv(gym.Env):
         images = {}
         for camera_name in self.camera_name:
             image = raw_obs[camera_name]
+            image = image[::-1, ::-1]  # rotate 180 degrees
             images[self.camera_name_mapping[camera_name]] = image
-
-        eef_pos = raw_obs.get("robot0_eef_pos")
-        eef_quat = raw_obs.get("robot0_eef_quat")
-
-        # rotation matrix from controller
-        eef_mat = self._env.robots[0].controller.ee_ori_mat if eef_pos is not None else None
-        gripper_qpos = raw_obs.get("robot0_gripper_qpos")
-        gripper_qvel = raw_obs.get("robot0_gripper_qvel")
-        joint_pos = raw_obs.get("robot0_joint_pos")
-        joint_vel = raw_obs.get("robot0_joint_vel")
-        obs = {
-            "pixels": images,
-            "robot_state": {
-                "eef": {
-                    "pos": eef_pos,  # (3,)
-                    "quat": eef_quat,  # (4,)
-                    "mat": eef_mat,  # (3, 3)
-                },
-                "gripper": {
-                    "qpos": gripper_qpos,  # (2,)
-                    "qvel": gripper_qvel,  # (2,)
-                },
-                "joints": {
-                    "pos": joint_pos,  # (7,)
-                    "vel": joint_vel,  # (7,)
-                },
-            },
-        }
+        state = np.concatenate(
+            (
+                raw_obs["robot0_eef_pos"],
+                quat2axisangle(raw_obs["robot0_eef_quat"]),
+                raw_obs["robot0_gripper_qpos"],
+            )
+        )
+        agent_pos = state
         if self.obs_type == "pixels":
             return {"pixels": images.copy()}
-
         if self.obs_type == "pixels_agent_pos":
-            # Validate required fields are present
-            if eef_pos is None or eef_quat is None or gripper_qpos is None:
-                raise ValueError(
-                    f"Missing required robot state fields in raw observation. "
-                    f"Got eef_pos={eef_pos is not None}, eef_quat={eef_quat is not None}, "
-                    f"gripper_qpos={gripper_qpos is not None}"
-                )
-            return obs
-
+            return {
+                "pixels": images.copy(),
+                "agent_pos": agent_pos,
+            }
         raise NotImplementedError(
             f"The observation type '{self.obs_type}' is not supported in LiberoEnv. "
             "Please switch to an image-based obs_type (e.g. 'pixels', 'pixels_agent_pos')."
@@ -300,15 +246,6 @@ class LiberoEnv(gym.Env):
         # Increasing this value can improve determinism and reproducibility across resets.
         for _ in range(self.num_steps_wait):
             raw_obs, _, _, _ = self._env.step(get_libero_dummy_action())
-
-        if self.control_mode == "absolute":
-            for robot in self._env.robots:
-                robot.controller.use_delta = False
-        elif self.control_mode == "relative":
-            for robot in self._env.robots:
-                robot.controller.use_delta = True
-        else:
-            raise ValueError(f"Invalid control mode: {self.control_mode}")
         observation = self._format_raw_obs(raw_obs)
         info = {"is_success": False}
         return observation, info
@@ -354,10 +291,8 @@ def _make_env_fns(
     task_id: int,
     n_envs: int,
     camera_names: list[str],
-    episode_length: int | None,
     init_states: bool,
     gym_kwargs: Mapping[str, Any],
-    control_mode: str,
 ) -> list[Callable[[], LiberoEnv]]:
     """Build n_envs factory callables for a single (suite, task_id)."""
 
@@ -369,9 +304,7 @@ def _make_env_fns(
             task_suite_name=suite_name,
             camera_name=camera_names,
             init_states=init_states,
-            episode_length=episode_length,
             episode_index=episode_index,
-            control_mode=control_mode,
             **local_kwargs,
         )
 
@@ -391,8 +324,6 @@ def create_libero_envs(
     camera_name: str | Sequence[str] = "agentview_image,robot0_eye_in_hand_image",
     init_states: bool = True,
     env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
-    control_mode: str = "relative",
-    episode_length: int | None = None,
 ) -> dict[str, dict[int, Any]]:
     """
     Create vectorized LIBERO environments with a consistent return shape.
@@ -424,24 +355,24 @@ def create_libero_envs(
         print(f"Restricting to task_ids={task_ids_filter}")
 
     out: dict[str, dict[int, Any]] = defaultdict(dict)
+
     for suite_name in suite_names:
         suite = _get_suite(suite_name)
         total = len(suite.tasks)
         selected = _select_task_ids(total, task_ids_filter)
+
         if not selected:
             raise ValueError(f"No tasks selected for suite '{suite_name}' (available: {total}).")
 
         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/envs/utils.py

@@ -13,8 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import importlib.util
-import os
 import warnings
 from collections.abc import Mapping, Sequence
 from functools import singledispatch
@@ -24,27 +22,14 @@ import einops
 import gymnasium as gym
 import numpy as np
 import torch
-from huggingface_hub import hf_hub_download, snapshot_download
 from torch import Tensor
 
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.envs.configs import EnvConfig
-from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE, OBS_STR
+from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
 from lerobot.utils.utils import get_channel_first_image_shape
 
 
-def _convert_nested_dict(d):
-    result = {}
-    for k, v in d.items():
-        if isinstance(v, dict):
-            result[k] = _convert_nested_dict(v)
-        elif isinstance(v, np.ndarray):
-            result[k] = torch.from_numpy(v)
-        else:
-            result[k] = v
-    return result
-
-
 def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Tensor]:
     # TODO(aliberts, rcadene): refactor this to use features from the environment (no hardcoding)
     """Convert environment observation to LeRobot format observation.
@@ -90,14 +75,12 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
 
         return_observations[OBS_ENV_STATE] = env_state
 
-    if "agent_pos" in observations:
-        agent_pos = torch.from_numpy(observations["agent_pos"]).float()
-        if agent_pos.dim() == 1:
-            agent_pos = agent_pos.unsqueeze(0)
-        return_observations[OBS_STATE] = agent_pos
+    # TODO(rcadene): enable pixels only baseline with `obs_type="pixels"` in environment by removing
+    agent_pos = torch.from_numpy(observations["agent_pos"]).float()
+    if agent_pos.dim() == 1:
+        agent_pos = agent_pos.unsqueeze(0)
+    return_observations[OBS_STATE] = agent_pos
 
-    if "robot_state" in observations:
-        return_observations[f"{OBS_STR}.robot_state"] = _convert_nested_dict(observations["robot_state"])
     return return_observations
 
 
@@ -212,132 +195,3 @@ def _(envs: Sequence) -> None:
 @close_envs.register
 def _(env: gym.Env) -> None:
     _close_single_env(env)
-
-
-# helper to safely load a python file as a module
-def _load_module_from_path(path: str, module_name: str | None = None):
-    module_name = module_name or f"hub_env_{os.path.basename(path).replace('.', '_')}"
-    spec = importlib.util.spec_from_file_location(module_name, path)
-    if spec is None:
-        raise ImportError(f"Could not load module spec for {module_name} from {path}")
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-
-
-# helper to parse hub string (supports "user/repo", "user/repo@rev", optional path)
-# examples:
-#   "user/repo" -> will look for env.py at repo root
-#   "user/repo@main:envs/my_env.py" -> explicit revision and path
-def _parse_hub_url(hub_uri: str):
-    # very small parser: [repo_id][@revision][:path]
-    # repo_id is required (user/repo or org/repo)
-    revision = None
-    file_path = "env.py"
-    if "@" in hub_uri:
-        repo_and_rev, *rest = hub_uri.split(":", 1)
-        repo_id, rev = repo_and_rev.split("@", 1)
-        revision = rev
-        if rest:
-            file_path = rest[0]
-    else:
-        repo_id, *rest = hub_uri.split(":", 1)
-        if rest:
-            file_path = rest[0]
-    return repo_id, revision, file_path
-
-
-def _download_hub_file(
-    cfg_str: str,
-    trust_remote_code: bool,
-    hub_cache_dir: str | None,
-) -> tuple[str, str, str, str]:
-    """
-    Parse `cfg_str` (hub URL), enforce `trust_remote_code`, and return
-    (repo_id, file_path, local_file, revision).
-    """
-    if not trust_remote_code:
-        raise RuntimeError(
-            f"Refusing to execute remote code from the Hub for '{cfg_str}'. "
-            "Executing hub env modules runs arbitrary Python code from third-party repositories. "
-            "If you trust this repo and understand the risks, call `make_env(..., trust_remote_code=True)` "
-            "and prefer pinning to a specific revision: 'user/repo@<commit-hash>:env.py'."
-        )
-
-    repo_id, revision, file_path = _parse_hub_url(cfg_str)
-
-    try:
-        local_file = hf_hub_download(
-            repo_id=repo_id, filename=file_path, revision=revision, cache_dir=hub_cache_dir
-        )
-    except Exception as e:
-        # fallback to snapshot download
-        snapshot_dir = snapshot_download(repo_id=repo_id, revision=revision, cache_dir=hub_cache_dir)
-        local_file = os.path.join(snapshot_dir, file_path)
-        if not os.path.exists(local_file):
-            raise FileNotFoundError(
-                f"Could not find {file_path} in repository {repo_id}@{revision or 'main'}"
-            ) from e
-
-    return repo_id, file_path, local_file, revision
-
-
-def _import_hub_module(local_file: str, repo_id: str) -> Any:
-    """
-    Import the downloaded file as a module and surface helpful import error messages.
-    """
-    module_name = f"hub_env_{repo_id.replace('/', '_')}"
-    try:
-        module = _load_module_from_path(local_file, module_name=module_name)
-    except ModuleNotFoundError as e:
-        missing = getattr(e, "name", None) or str(e)
-        raise ModuleNotFoundError(
-            f"Hub env '{repo_id}:{os.path.basename(local_file)}' failed to import because the dependency "
-            f"'{missing}' is not installed locally.\n\n"
-        ) from e
-    except ImportError as e:
-        raise ImportError(
-            f"Failed to load hub env module '{repo_id}:{os.path.basename(local_file)}'. Import error: {e}\n\n"
-        ) from e
-    return module
-
-
-def _call_make_env(module: Any, n_envs: int, use_async_envs: bool) -> Any:
-    """
-    Ensure module exposes make_env and call it.
-    """
-    if not hasattr(module, "make_env"):
-        raise AttributeError(
-            f"The hub module {getattr(module, '__name__', 'hub_module')} must expose `make_env(n_envs=int, use_async_envs=bool)`."
-        )
-    entry_fn = module.make_env
-    return entry_fn(n_envs=n_envs, use_async_envs=use_async_envs)
-
-
-def _normalize_hub_result(result: Any) -> dict[str, dict[int, gym.vector.VectorEnv]]:
-    """
-    Normalize possible return types from hub `make_env` into the mapping:
-      { suite_name: { task_id: vector_env } }
-    Accepts:
-      - dict (assumed already correct)
-      - gym.vector.VectorEnv
-      - gym.Env (will be wrapped into SyncVectorEnv)
-    """
-    if isinstance(result, dict):
-        return result
-
-    # VectorEnv: use its spec.id if available
-    if isinstance(result, gym.vector.VectorEnv):
-        suite_name = getattr(result, "spec", None) and getattr(result.spec, "id", None) or "hub_env"
-        return {suite_name: {0: result}}
-
-    # Single Env: wrap into SyncVectorEnv
-    if isinstance(result, gym.Env):
-        vec = gym.vector.SyncVectorEnv([lambda: result])
-        suite_name = getattr(result, "spec", None) and getattr(result.spec, "id", None) or "hub_env"
-        return {suite_name: {0: vec}}
-
-    raise ValueError(
-        "Hub `make_env` must return either a mapping {suite: {task_id: vec_env}}, "
-        "a gym.vector.VectorEnv, or a single gym.Env."
-    )

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

@@ -14,5 +14,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .config_unitree_g1 import UnitreeG1Config
-from .unitree_g1 import UnitreeG1
+from .damiao import DamiaoMotorsBus
+from .tables import *

src/lerobot/motors/damiao/damiao.py

@@ -0,0 +1,787 @@
+# 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=0.0001)  # 100us poll timeout
+                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 _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=0.003)  # 3ms 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_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)
+            
+            # 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=0.002)  # 2ms timeout
+        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/seed_damiao.py

@@ -0,0 +1,833 @@
+## This is a derivative of the following software.
+## https://github.com/cmjang/DM_Control_Python/blob/main/DM_CAN.py
+
+import can
+from time import sleep, time
+import numpy as np
+from enum import IntEnum
+from struct import unpack
+from struct import pack
+
+class Motor:
+    def __init__(self, MotorType, SlaveID, MasterID):
+        """
+        define Motor object 定义电机对象
+        :param MotorType: Motor type 电机类型
+        :param SlaveID: CANID 电机ID
+        :param MasterID: MasterID 主机ID 建议不要设为0
+        """
+        self.Pd = float(0)
+        self.Vd = float(0)
+        self.goal_position = float(0)
+        self.goal_tau = float(0)
+        self.state_q = float(0)
+        self.state_dq = float(0)
+        self.state_tau = float(0)
+        self.state_tmos = int(0)
+        self.state_trotor = int(0)
+        self.SlaveID = SlaveID
+        self.MasterID = MasterID
+        self.MotorType = MotorType
+        self.isEnable = False
+        self.NowControlMode = Control_Type.MIT
+        self.temp_param_dict = {}
+
+    def recv_data(self, q: float, dq: float, tau: float, tmos: int, trotor: int):
+        self.state_q = q
+        self.state_dq = dq
+        self.state_tau = tau
+        self.state_tmos = tmos
+        self.state_trotor = trotor
+
+    def getPosition(self):
+        """
+        get the position of the motor 获取电机位置
+        :return: the position of the motor 电机位置
+        """
+        return self.state_q
+
+    def getVelocity(self):
+        """
+        get the velocity of the motor 获取电机速度
+        :return: the velocity of the motor 电机速度
+        """
+        return self.state_dq
+
+    def getTorque(self):
+        """
+        get the torque of the motor 获取电机力矩
+        :return: the torque of the motor 电机力矩
+        """
+        return self.state_tau
+
+    def getParam(self, RID):
+        """
+        get the parameter of the motor 获取电机内部的参数，需要提前读取
+        :param RID: DM_variable 电机参数
+        :return: the parameter of the motor 电机参数
+        """
+        if RID in self.temp_param_dict:
+            return self.temp_param_dict[RID]
+        else:
+            return None
+
+
+class MotorControl:
+    #send_data_frame = np.array(
+    #    [0x55, 0xAA, 0x1e, 0x03, 0x01, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0, 0, 0, 0, 0x00, 0x08, 0x00,
+    #     0x00, 0, 0, 0, 0, 0, 0, 0, 0, 0x00], np.uint8)
+    #                4310           4310_48        4340           4340_48
+    Limit_Param = [[12.5, 30, 10], [12.5, 50, 10], [12.5, 8, 28], [12.5, 10, 28],
+                   # 6006           8006           8009            10010L         10010
+                   [12.5, 45, 20], [12.5, 45, 40], [12.5, 45, 54], [12.5, 25, 200], [12.5, 20, 200],
+                   # H3510            DMG62150      DMH6220
+                   [12.5 , 280 , 1],[12.5 , 45 , 10],[12.5 , 45 , 10]]
+
+    def __init__(self, channel: str, bitrate: int = 1000000):
+        """
+        define MotorControl object 定义电机控制对象
+        :param serial_device: serial object 串口对象
+        """
+        #self.serial_ = serial_device
+        self.motors_map = dict()
+        self.data_save = bytes()  # save data
+        #if self.serial_.is_open:  # open the serial port
+        #    print("Serial port is open")
+        #    serial_device.close()
+        #self.serial_.open()
+        self.canbus = can.interface.Bus(channel=channel, interface='socketcan', bitrate=bitrate)
+
+        #print("can is open")
+        
+
+    def controlMIT(self, DM_Motor, kp: float, kd: float, q: float, dq: float, tau: float):
+        """
+        MIT Control Mode Function 达妙电机MIT控制模式函数
+        :param DM_Motor: Motor object 电机对象
+        :param kp: kp
+        :param kd:  kd
+        :param q:  position  期望位置
+        :param dq:  velocity  期望速度
+        :param tau: torque  期望力矩
+        :return: None
+        """
+        if DM_Motor.SlaveID not in self.motors_map:
+            print("controlMIT ERROR : Motor ID not found")
+            return
+        kp_uint = float_to_uint(kp, 0, 500, 12)
+        kd_uint = float_to_uint(kd, 0, 5, 12)
+        MotorType = DM_Motor.MotorType
+        Q_MAX = self.Limit_Param[MotorType][0]
+        DQ_MAX = self.Limit_Param[MotorType][1]
+        TAU_MAX = self.Limit_Param[MotorType][2]
+        q_uint = float_to_uint(q, -Q_MAX, Q_MAX, 16)
+        dq_uint = float_to_uint(dq, -DQ_MAX, DQ_MAX, 12)
+        tau_uint = float_to_uint(tau, -TAU_MAX, TAU_MAX, 12)
+        data_buf = np.array([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00], np.uint8)
+        data_buf[0] = (q_uint >> 8) & 0xff
+        data_buf[1] = q_uint & 0xff
+        data_buf[2] = dq_uint >> 4
+        data_buf[3] = ((dq_uint & 0xf) << 4) | ((kp_uint >> 8) & 0xf)
+        data_buf[4] = kp_uint & 0xff
+        data_buf[5] = kd_uint >> 4
+        data_buf[6] = ((kd_uint & 0xf) << 4) | ((tau_uint >> 8) & 0xf)
+        data_buf[7] = tau_uint & 0xff
+        self.__send_data(DM_Motor.SlaveID, data_buf)
+        self.recv()  # receive the data from serial port
+
+    def control_delay(self, DM_Motor, kp: float, kd: float, q: float, dq: float, tau: float, delay: float):
+        """
+        MIT Control Mode Function with delay 达妙电机MIT控制模式函数带延迟
+        :param DM_Motor: Motor object 电机对象
+        :param kp: kp
+        :param kd: kd
+        :param q:  position  期望位置
+        :param dq:  velocity  期望速度
+        :param tau: torque  期望力矩
+        :param delay: delay time 延迟时间 单位秒
+        """
+        self.controlMIT(DM_Motor, kp, kd, q, dq, tau)
+        sleep(delay)
+
+    def control_Pos_Vel(self, Motor, P_desired: float, V_desired: float):
+        """
+        control the motor in position and velocity control mode 电机位置速度控制模式
+        :param Motor: Motor object 电机对象
+        :param P_desired: desired position 期望位置
+        :param V_desired: desired velocity 期望速度
+        :return: None
+        """
+        if Motor.SlaveID not in self.motors_map:
+            print("Control Pos_Vel Error : Motor ID not found")
+            return
+        motorid = 0x100 + Motor.SlaveID
+        data_buf = np.array([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00], np.uint8)
+        P_desired_uint8s = float_to_uint8s(P_desired)
+        V_desired_uint8s = float_to_uint8s(V_desired)
+        data_buf[0:4] = P_desired_uint8s
+        data_buf[4:8] = V_desired_uint8s
+        self.__send_data(motorid, data_buf)
+        self.recv()  # receive the data from serial port
+
+    def control_Vel(self, Motor, Vel_desired):
+        """
+        control the motor in velocity control mode 电机速度控制模式
+        :param Motor: Motor object 电机对象
+        :param Vel_desired: desired velocity 期望速度
+        """
+        if Motor.SlaveID not in self.motors_map:
+            print("control_VEL ERROR : Motor ID not found")
+            return
+        motorid = 0x200 + Motor.SlaveID
+        data_buf = np.array([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00], np.uint8)
+        Vel_desired_uint8s = float_to_uint8s(Vel_desired)
+        data_buf[0:4] = Vel_desired_uint8s
+        self.__send_data(motorid, data_buf)
+        self.recv()  # receive the data from serial port
+
+    def control_pos_force(self, Motor, Pos_des: float, Vel_des, i_des):
+        """
+        control the motor in EMIT control mode 电机力位混合模式
+        :param Pos_des: desired position rad  期望位置 单位为rad
+        :param Vel_des: desired velocity rad/s  期望速度 为放大100倍
+        :param i_des: desired current rang 0-10000 期望电流标幺值放大10000倍
+        电流标幺值：实际电流值除以最大电流值，最大电流见上电打印
+        """
+        if Motor.SlaveID not in self.motors_map:
+            print("control_pos_vel ERROR : Motor ID not found")
+            return
+        motorid = 0x300 + Motor.SlaveID
+        data_buf = np.array([0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00], np.uint8)
+        Pos_desired_uint8s = float_to_uint8s(Pos_des)
+        data_buf[0:4] = Pos_desired_uint8s
+        Vel_uint = np.uint16(Vel_des)
+        ides_uint = np.uint16(i_des)
+        data_buf[4] = Vel_uint & 0xff
+        data_buf[5] = Vel_uint >> 8
+        data_buf[6] = ides_uint & 0xff
+        data_buf[7] = ides_uint >> 8
+        self.__send_data(motorid, data_buf)
+        self.recv()  # receive the data from serial port
+
+    def enable(self, Motor):
+        """
+        enable motor 使能电机
+        最好在上电后几秒后再使能电机
+        :param Motor: Motor object 电机对象
+        """
+        self.__control_cmd(Motor, np.uint8(0xFC))
+        sleep(0.1)
+        self.recv()  # receive the data from serial port
+
+    def enable_old(self, Motor ,ControlMode):
+        """
+        enable motor old firmware 使能电机旧版本固件，这个是为了旧版本电机固件的兼容性
+        可恶的旧版本固件使能需要加上偏移量
+        最好在上电后几秒后再使能电机
+        :param Motor: Motor object 电机对象
+        """
+        data_buf = np.array([0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xfc], np.uint8)
+        enable_id = ((int(ControlMode)-1) << 2) + Motor.SlaveID
+        self.__send_data(enable_id, data_buf)
+        sleep(0.1)
+        self.recv()  # receive the data from serial port
+
+    def disable(self, Motor):
+        """
+        disable motor 失能电机
+        :param Motor: Motor object 电机对象
+        """
+        self.__control_cmd(Motor, np.uint8(0xFD))
+        sleep(0.1)
+        self.recv()  # receive the data from serial port
+
+    def set_zero_position(self, Motor):
+        """
+        set the zero position of the motor 设置电机0位
+        :param Motor: Motor object 电机对象
+        """
+        self.__control_cmd(Motor, np.uint8(0xFE))
+        sleep(0.1)
+        self.recv()  # receive the data from serial port
+
+    def recv(self):
+        # 把上次没有解析完的剩下的也放进来
+        # data_recv = b''.join([self.data_save, self.serial_.read_all()])
+        #data_recv = b''.join([self.data_save, self.canbus.recv()])
+
+
+        # packets = self.__extract_packets(data_recv)
+        # for packet in packets:
+        #     data = packet[7:15]
+        #     CANID = (packet[6] << 24) | (packet[5] << 16) | (packet[4] << 8) | packet[3]
+        #     CMD = packet[1]
+        #     self.__process_packet(data, CANID, CMD)
+        
+        data_recv = self.canbus.recv(0.1)
+
+        if data_recv is not None:
+            # data = data_recv.data
+            # err = data[0] >> 12
+            # id = data[0] & 0x7f
+            # pos = (data[1] << 8) + data[2]
+            # vel = (data[3] << 4) + (data[4] >> 4)
+            # tau = ((data[4] & 0x0f) << 8) + data[5]
+            # t_mos = data[6]
+            # t_rotor = data[7]
+            # print(hex(id), err, id, pos, vel, tau, goal_tau, t_mos, t_rotor)
+            # CANID = data_recv.arbitration_id
+            CANID = data_recv.data[0]
+            # CMD = data_recv.data[3]
+            CMD = 0x11                  # 飯田：修正の必要あり
+            self.__process_packet(data_recv.data, CANID, CMD)
+
+            # 飯田：Debug print
+            # print(hex(CANID),hex(CMD))
+            # print(hex(data_recv.data[0]),hex(data_recv.data[1]),hex(data_recv.data[2]),hex(data_recv.data[3]),hex(data_recv.data[4]),hex(data_recv.data[5]),hex(data_recv.data[6]),hex(data_recv.data[7]))
+            #return data
+
+    def recv_set_param_data(self):
+        #data_recv = self.serial_.read_all()
+
+        # packets = self.__extract_packets(data_recv)
+        # for packet in packets:
+        #     data = packet[7:15]
+        #     CANID = (packet[6] << 24) | (packet[5] << 16) | (packet[4] << 8) | packet[3]
+        #     CMD = packet[1]
+        #     self.__process_set_param_packet(data, CANID, CMD)
+        
+        data_recv = self.canbus.recv(0.1)
+
+
+        if data_recv is not None:
+            data = data_recv.data
+            CANID = data_recv.arbitration_id
+            # CANID = data_recv.data[0]
+            # CMD = data_recv.data[3]  
+            CMD = 0x11                  # 飯田：修正の必要あり
+            self.__process_packet(data, CANID, CMD)
+
+
+            # 飯田：Debug print
+            print(hex(CANID),hex(CMD))
+            print(hex(data_recv.data[0]),hex(data_recv.data[1]),hex(data_recv.data[2]),hex(data_recv.data[3]),hex(data_recv.data[4]),hex(data_recv.data[5]),hex(data_recv.data[6]),hex(data_recv.data[7]))
+
+    def __process_packet(self, data, CANID, CMD):
+        if CMD == 0x11:
+            if CANID != 0x00:
+                if CANID in self.motors_map:
+                    q_uint = np.uint16((np.uint16(data[1]) << 8) | data[2])
+                    dq_uint = np.uint16((np.uint16(data[3]) << 4) | (data[4] >> 4))
+                    tau_uint = np.uint16(((data[4] & 0xf) << 8) | data[5])
+                    t_mos = data[6]
+                    t_rotor = data[7]
+                    MotorType_recv = self.motors_map[CANID].MotorType
+                    Q_MAX = self.Limit_Param[MotorType_recv][0]
+                    DQ_MAX = self.Limit_Param[MotorType_recv][1]
+                    TAU_MAX = self.Limit_Param[MotorType_recv][2]
+                    recv_q = uint_to_float(q_uint, -Q_MAX, Q_MAX, 16)
+                    recv_dq = uint_to_float(dq_uint, -DQ_MAX, DQ_MAX, 12)
+                    recv_tau = uint_to_float(tau_uint, -TAU_MAX, TAU_MAX, 12)
+                    self.motors_map[CANID].recv_data(recv_q, recv_dq, recv_tau, t_mos, t_rotor)
+            else:
+                MasterID=data[0] & 0x0f
+                if MasterID in self.motors_map:
+                    q_uint = np.uint16((np.uint16(data[1]) << 8) | data[2])
+                    dq_uint = np.uint16((np.uint16(data[3]) << 4) | (data[4] >> 4))
+                    tau_uint = np.uint16(((data[4] & 0xf) << 8) | data[5])
+                    t_mos = data[6]
+                    t_rotor = data[7]
+                    MotorType_recv = self.motors_map[MasterID].MotorType
+                    Q_MAX = self.Limit_Param[MotorType_recv][0]
+                    DQ_MAX = self.Limit_Param[MotorType_recv][1]
+                    TAU_MAX = self.Limit_Param[MotorType_recv][2]
+                    recv_q = uint_to_float(q_uint, -Q_MAX, Q_MAX, 16)
+                    recv_dq = uint_to_float(dq_uint, -DQ_MAX, DQ_MAX, 12)
+                    recv_tau = uint_to_float(tau_uint, -TAU_MAX, TAU_MAX, 12)
+                    self.motors_map[MasterID].recv_data(recv_q, recv_dq, recv_tau, t_mos, t_rotor)
+
+
+    def __process_set_param_packet(self, data, CANID, CMD):
+        if CMD == 0x11 and (data[2] == 0x33 or data[2] == 0x55):
+            masterid=CANID
+            slaveId = ((data[1] << 8) | data[0])
+            if CANID==0x00:  #防止有人把MasterID设为0稳一手
+                masterid=slaveId
+
+            if masterid not in self.motors_map:
+                if slaveId not in self.motors_map:
+                    return
+                else:
+                    masterid=slaveId
+
+            RID = data[3]
+            # 读取参数得到的数据
+            if is_in_ranges(RID):
+                #uint32类型
+                num = uint8s_to_uint32(data[4], data[5], data[6], data[7])
+                self.motors_map[masterid].temp_param_dict[RID] = num
+
+            else:
+                #float类型
+                num = uint8s_to_float(data[4], data[5], data[6], data[7])
+                self.motors_map[masterid].temp_param_dict[RID] = num
+
+
+    def addMotor(self, Motor):
+        """
+        add motor to the motor control object 添加电机到电机控制对象
+        :param Motor: Motor object 电机对象
+        """
+        self.motors_map[Motor.SlaveID] = Motor
+        if Motor.MasterID != 0:
+            self.motors_map[Motor.MasterID] = Motor
+        return True
+
+    def __control_cmd(self, Motor, cmd: np.uint8):     # 飯田：コマンドは通ります
+        data_buf = np.array([0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, cmd], np.uint8)
+        self.__send_data(Motor.SlaveID, data_buf)
+
+    def __send_data(self, motor_id, data):
+        """
+        send data to the motor 发送数据到电机
+        :param motor_id:
+        :param data:
+        :return:
+        """
+        #self.send_data_frame[13] = motor_id & 0xff
+        #self.send_data_frame[14] = (motor_id >> 8)& 0xff  #id high 8 bits
+        #self.send_data_frame[21:29] = data
+        #self.serial_.write(bytes(self.send_data_frame.T))
+
+        msg =can.Message(is_extended_id=False,arbitration_id=motor_id,data=data,is_remote_frame = False)
+        self.canbus.send(msg)
+
+    def __read_RID_param(self, Motor, RID):             # 飯田：修正の必要あり?
+        can_id_l = Motor.SlaveID & 0xff #id low 8 bits
+        can_id_h = (Motor.SlaveID >> 8)& 0xff  #id high 8 bits
+        data_buf = np.array([np.uint8(can_id_l), np.uint8(can_id_h), 0x33, np.uint8(RID), 0x00, 0x00, 0x00, 0x00], np.uint8)
+        self.__send_data(0x7FF, data_buf)
+        
+
+
+    def __write_motor_param(self, Motor, RID, data):             # 飯田：修正の必要あり?
+        can_id_l = Motor.SlaveID & 0xff #id low 8 bits
+        can_id_h = (Motor.SlaveID >> 8)& 0xff  #id high 8 bits
+        data_buf = np.array([np.uint8(can_id_l), np.uint8(can_id_h), 0x55, np.uint8(RID), 0x00, 0x00, 0x00, 0x00], np.uint8)
+        if not is_in_ranges(RID):
+            # data is float
+            data_buf[4:8] = float_to_uint8s(data)
+        else:
+            # data is int
+            data_buf[4:8] = data_to_uint8s(int(data))
+        self.__send_data(0x7FF, data_buf)
+
+    def switchControlMode(self, Motor, ControlMode):
+        """
+        switch the control mode of the motor 切换电机控制模式
+        :param Motor: Motor object 电机对象
+        :param ControlMode: Control_Type 电机控制模式 example:MIT:Control_Type.MIT MIT模式
+        """
+        max_retries = 20
+        retry_interval = 0.1  #retry times
+        RID = 10
+        self.__write_motor_param(Motor, RID, np.uint8(ControlMode))
+        for _ in range(max_retries):
+            sleep(retry_interval)
+            self.recv_set_param_data()
+            if Motor.SlaveID in self.motors_map:
+                if RID in self.motors_map[Motor.SlaveID].temp_param_dict:
+                    if abs(self.motors_map[Motor.SlaveID].temp_param_dict[RID] - ControlMode) < 0.1:
+                        return True
+                    else:
+                        return False
+        return False
+
+    def save_motor_param(self, Motor):
+        """
+        save the all parameter  to flash 保存所有电机参数
+        :param Motor: Motor object 电机对象
+        :return:
+        """
+        can_id_l = Motor.SlaveID & 0xff #id low 8 bits
+        can_id_h = (Motor.SlaveID >> 8)& 0xff  #id high 8 bits
+        data_buf = np.array([np.uint8(can_id_l), np.uint8(can_id_h), 0xAA, 0x00, 0x00, 0x00, 0x00, 0x00], np.uint8)
+        self.disable(Motor)  # before save disable the motor
+        self.__send_data(0x7FF, data_buf)
+        sleep(0.001)
+
+    def change_limit_param(self, Motor_Type, PMAX, VMAX, TMAX):
+        """
+        change the PMAX VMAX TMAX of the motor 改变电机的PMAX VMAX TMAX
+        :param Motor_Type:
+        :param PMAX: 电机的PMAX
+        :param VMAX: 电机的VMAX
+        :param TMAX: 电机的TMAX
+        :return:
+        """
+        self.Limit_Param[Motor_Type][0] = PMAX
+        self.Limit_Param[Motor_Type][1] = VMAX
+        self.Limit_Param[Motor_Type][2] = TMAX
+
+    def refresh_motor_status(self,Motor):
+        """
+        get the motor status 获得电机状态
+        """
+        can_id_l = Motor.SlaveID & 0xff #id low 8 bits
+        can_id_h = (Motor.SlaveID >> 8) & 0xff  #id high 8 bits
+        data_buf = np.array([np.uint8(can_id_l), np.uint8(can_id_h), 0xCC, 0x00, 0x00, 0x00, 0x00, 0x00], np.uint8)
+        self.__send_data(0x7FF, data_buf)
+        self.recv()  # receive the data from serial port
+
+    def change_motor_param(self, Motor, RID, data):
+        """
+        change the RID of the motor 改变电机的参数
+        :param Motor: Motor object 电机对象
+        :param RID: DM_variable 电机参数
+        :param data: 电机参数的值
+        :return: True or False ,True means success, False means fail
+        """
+        max_retries = 20
+        retry_interval = 0.05  #retry times
+
+        self.__write_motor_param(Motor, RID, data)
+        for _ in range(max_retries):
+            self.recv_set_param_data()
+            if Motor.SlaveID in self.motors_map and RID in self.motors_map[Motor.SlaveID].temp_param_dict:
+                if abs(self.motors_map[Motor.SlaveID].temp_param_dict[RID] - data) < 0.1:
+                    return True
+                else:
+                    return False
+            sleep(retry_interval)
+        return False
+
+    def read_motor_param(self, Motor, RID):
+        """
+        read only the RID of the motor 读取电机的内部信息例如 版本号等
+        :param Motor: Motor object 电机对象
+        :param RID: DM_variable 电机参数
+        :return: 电机参数的值
+        """
+        max_retries = 5
+        retry_interval = 0.05  #retry times
+        self.__read_RID_param(Motor, RID)
+        for _ in range(max_retries):
+            sleep(retry_interval)
+            self.recv_set_param_data()
+            if Motor.SlaveID in self.motors_map:
+                if RID in self.motors_map[Motor.SlaveID].temp_param_dict:
+                    return self.motors_map[Motor.SlaveID].temp_param_dict[RID]
+        return None
+
+    # -------------------------------------------------
+    # Extract packets from the serial data
+    def __extract_packets(self, data):          
+        frames = []
+        header = 0xAA
+        tail = 0x55
+        frame_length = 16
+        i = 0
+        remainder_pos = 0
+
+        while i <= len(data) - frame_length:
+            if data[i] == header and data[i + frame_length - 1] == tail:
+                frame = data[i:i + frame_length]
+                frames.append(frame)
+                i += frame_length
+                remainder_pos = i
+            else:
+                i += 1
+        self.data_save = data[remainder_pos:]
+        return frames
+
+
+def LIMIT_MIN_MAX(x, min, max):
+    if x <= min:
+        x = min
+    elif x > max:
+        x = max
+
+
+def float_to_uint(x: float, x_min: float, x_max: float, bits):
+    LIMIT_MIN_MAX(x, x_min, x_max)
+    span = x_max - x_min
+    data_norm = (x - x_min) / span
+    return np.uint16(data_norm * ((1 << bits) - 1))
+
+
+def uint_to_float(x: np.uint16, min: float, max: float, bits):
+    span = max - min
+    data_norm = float(x) / ((1 << bits) - 1)
+    temp = data_norm * span + min
+    return np.float32(temp)
+
+
+def float_to_uint8s(value):
+    # Pack the float into 4 bytes
+    packed = pack('f', value)
+    # Unpack the bytes into four uint8 values
+    return unpack('4B', packed)
+
+
+def data_to_uint8s(value):
+    # Check if the value is within the range of uint32
+    if isinstance(value, int) and (0 <= value <= 0xFFFFFFFF):
+        # Pack the uint32 into 4 bytes
+        packed = pack('I', value)
+    else:
+        raise ValueError("Value must be an integer within the range of uint32")
+
+    # Unpack the bytes into four uint8 values
+    return unpack('4B', packed)
+
+
+def is_in_ranges(number):
+    """
+    check if the number is in the range of uint32
+    :param number:
+    :return:
+    """
+    if (7 <= number <= 10) or (13 <= number <= 16) or (35 <= number <= 36):
+        return True
+    return False
+
+
+def uint8s_to_uint32(byte1, byte2, byte3, byte4):
+    # Pack the four uint8 values into a single uint32 value in little-endian order
+    packed = pack('<4B', byte1, byte2, byte3, byte4)
+    # Unpack the packed bytes into a uint32 value
+    return unpack('<I', packed)[0]
+
+
+def uint8s_to_float(byte1, byte2, byte3, byte4):
+    # Pack the four uint8 values into a single float value in little-endian order
+    packed = pack('<4B', byte1, byte2, byte3, byte4)
+    # Unpack the packed bytes into a float value
+    return unpack('<f', packed)[0]
+
+
+def print_hex(data):
+    hex_values = [f'{byte:02X}' for byte in data]
+    print(' '.join(hex_values))
+
+
+def get_enum_by_index(index, enum_class):
+    try:
+        return enum_class(index)
+    except ValueError:
+        return None
+
+
+class DM_Motor_Type(IntEnum):
+    DM4310 = 0
+    DM4310_48V = 1
+    DM4340 = 2
+    DM4340_48V = 3
+    DM6006 = 4
+    DM8006 = 5
+    DM8009 = 6
+    DM10010L = 7
+    DM10010 = 8
+    DMH3510 = 9
+    DMH6215 = 10
+    DMG6220 = 11
+
+
+class DM_variable(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
+
+
+class Control_Type(IntEnum):
+    MIT = 1
+    POS_VEL = 2
+    VEL = 3
+    Torque_Pos = 4
+
+class DamiaoPort:
+    def __init__(self, device, types, can_ids, master_ids, motor_with_torque, control_mode=Control_Type.MIT):
+        self.device = device
+        self.types = types
+        self.can_ids = can_ids
+        self.master_ids = master_ids
+        self.control = MotorControl(self.device, bitrate=4000000)
+        self.motors = [Motor(type, can_id, master_id) for type, can_id, master_id in zip(types, can_ids, master_ids)]
+        self.stat_data = []
+        self.stat_time = []
+        for motor in self.motors:
+            self.control.addMotor(motor)
+            self.control.enable(motor)
+
+    def get_present_status(self):
+        self.stat_time.append(time())
+        stat = [[
+            motor.goal_position,
+            motor.goal_tau,
+            motor.getPosition(),
+            motor.getVelocity(),
+            motor.getTorque(),
+            motor.state_tmos,
+            motor.state_trotor,
+        ] for motor in self.motors]
+        self.stat_data.append(stat)
+
+        return stat
+
+    def save_status(self, filename):
+        np.savez(filename, np.array(self.stat_time), np.array(self.stat_data))
+
+    def disable(self):
+        for motor in self.motors:
+            self.control.disable(motor)
+
+    def shutdown(self):
+        for motor in self.motors:
+            self.control.controlMIT(motor, 0, 0, 0, 0, 0)
+        self.control.canbus.shutdown()
+
+    def set_zero_position(self):
+        for motor in self.motors:
+            self.control.disable(motor)
+        sleep(1)
+        for motor in self.motors:
+            self.control.set_zero_position(motor)
+        sleep(1)
+        for motor in self.motors:
+            self.control.enable(motor)
+        return 0
+
+    async def move_towards(self, goal_positions, kps, kds):
+        for motor, goal_position, kp, kd in zip(self.motors, goal_positions, kps, kds):
+            delta = goal_position - motor.getPosition()
+            v = motor.getVelocity()
+            tau = kp * delta - kd * v
+            motor.goal_position = goal_position
+            motor.goal_tau = tau
+            self.control.controlMIT(motor, 0, 0, 0, 0, tau)
+            await asyncio.sleep(0.00003)
+
+    def move_regressor_sync(self, regs, search_range, search_step, goal_positions, kps, kds):
+        TORQUE_SCALER=30
+        if len(self.stat_data) == 0:
+            return self.move_towards_sync(goal_positions, kps, kds)
+        for motor, reg, goal_position, kp, kd, stat in zip(
+                self.motors, regs, goal_positions, kps, kds, self.stat_data[-1]):
+            pos = motor.getPosition()
+            vel = motor.getVelocity()
+            delta = goal_position - pos
+            goal_tau = kp * delta - kd * vel
+            _goal_pos, _goal_tau, _pos, _vel, _tau = stat
+            x = np.array([[_pos, _vel, _tau, _goal_pos, _goal_tau],
+                          [pos, vel, motor.getTorque(), goal_position, goal_tau]])
+            x /= np.array([[np.pi, 10, TORQUE_SCALER, np.pi, TORQUE_SCALER]])
+            xs = []
+            for tau in np.linspace(goal_tau/TORQUE_SCALER - search_range,
+                                   goal_tau/TORQUE_SCALER + search_range,
+                                   num=search_step):
+                x_ = x.copy()
+                x_[0,4] = tau
+                xs.append(x_.flatten())
+            h = reg.predict(xs)
+            diff = h - goal_position
+            tau = TORQUE_SCALER * xs[np.argmin(diff ** 2)][4]
+            goal_tau = tau
+            motor.goal_position = goal_position
+            motor.goal_tau = goal_tau
+            self.control.controlMIT(motor, 0, 0, 0, 0, goal_tau)
+            sleep(0.00003)
+
+    def move_towards_sync(self, goal_positions, kps, kds):
+        for motor, goal_position, kp, kd in zip(self.motors, goal_positions, kps, kds):
+            delta = goal_position - motor.getPosition()
+            v = motor.getVelocity()
+            tau = kp * delta - kd * v
+            motor.goal_position = goal_position
+            motor.goal_tau = tau
+            self.control.controlMIT(motor, 0, 0, 0, 0, tau)
+            # sleep(0.00003)
+
+    def set_goal_torque_sync(self, goal_taus):
+        for motor, goal_tau in zip(self.motors, goal_taus):
+            motor.goal_position = 0
+            motor.goal_tau = goal_tau
+            self.control.controlMIT(motor, 0, 0, 0, 0, motor.goal_tau)
+            sleep(0.00003)
+
+    def move_torque_sync(self, taus):
+        for motor,tau in zip(self.motors, taus):
+            motor.goal_position = 0
+            motor.goal_tau = tau
+            self.control.controlMIT(motor, 0, 0, 0, 0, motor.goal_tau)
+            sleep(0.00003)
+
+    def keep_torque_sync(self):
+        for motor in self.motors:
+            self.control.controlMIT(motor, 0, 0, 0, 0, motor.goal_tau)
+            sleep(0.00003)
+
+    async def set_goal_positions(self, goal_positions, kps):
+        for motor, goal_position, kp in zip(self.motors, goal_positions, kps):
+            motor.goal_position = goal_position
+            motor.goal_tau = 0
+            self.control.controlMIT(motor, kp, 1.2, goal_position, 0, 0)
+            await asyncio.sleep(0.00003)
+
+    def set_goal_positions_sync(self, goal_positions, kps, kds):
+        for motor, goal_position, kp, kd in zip(self.motors, goal_positions, kps, kds):
+            motor.goal_position = goal_position
+            motor.goal_tau = 0
+            self.control.controlMIT(motor, kp, kd, goal_position, 0, 0)
+            sleep(0.00003)
+
+    def set_goal_posvel(self, goal_positions):
+        for motor, goal_position in zip(self.motors, goal_positions):
+            motor.goal_position = goal_position
+            motor.goal_tau = 0
+            self.control.control_pos_force(motor, goal_position, 1, 1)
+
+    def controlMIT(self, motor, kp, kd, q, dq, tau):
+        self.control.controlMIT(self.motors[motor], kp, kd, q, dq, tau)
+

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.

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