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feat(rollout): decouple policy deployment from data recording with new lerobot-rollout CLI (#3413)

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* feat(scripts): lerobot-rollout

* fix(rollout) require dataset in dagger + use duration too

* fix(docs): dagger num_episodes

* test(rollout): fix expectations

* fix(rollout): features check

* fix(rollout): device and task propagation + feature pos + warn fps + move rename_map config

* docs(rollout): edit rename_map instructions

* chore(rollout): multiple minor improvements

* chore(rollout): address coments + minor improvements

* fix(rollout): enable default

* fix(tests): default value RTCConfig

* fix(rollout): robot_observation_processor and notify_observation at policy frequency instead of interpolator rate

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* fix(rollout): prevent relativeactions with sync inference engine

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* fix(rollout): rtc reanchor to non normalized state

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* fix(rollout): fixing the episode length to use hwc (#3469)

also reducing default length to 5 minutes

* feat(rollout): go back to initial position is now a config

* fix(rollout): properly propagating video_files_size_in_mb to lerobot_dataset (#3470)

* chore(rollout): note about dagger correction stage

* chore(docs): update comments and docstring

* fix(test): move rtc relative out of rollout module

* fix(rollout): address the review comments

---------

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co>
This commit is contained in:
Steven Palma
2026-04-28 00:57:35 +02:00
committed by GitHub
parent 77352c495c
commit ca87ccd941
62 changed files with 5577 additions and 2847 deletions
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examples/so100_to_so100_EE/record.py
+15 -17
View File
@@ -62,21 +62,20 @@ def main():
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
# 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()),
)
# Build pipeline to convert follower joints to EE observation
# Build pipeline to convert follower joints to EE observation.
follower_joints_to_ee = RobotProcessorPipeline[RobotObservation, RobotObservation](
steps=[
ForwardKinematicsJointsToEE(
@@ -87,7 +86,7 @@ def main():
to_output=transition_to_observation,
)
# Build pipeline to convert leader joints to EE action
# Build pipeline to convert leader joints to EE action.
leader_joints_to_ee = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
steps=[
ForwardKinematicsJointsToEE(
@@ -98,9 +97,9 @@ def main():
to_output=transition_to_robot_action,
)
# Build pipeline to convert EE action to follower joints
# Build pipeline to convert EE action to follower joints (with safety bounds).
ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
[
steps=[
EEBoundsAndSafety(
end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
max_ee_step_m=0.10,
@@ -115,13 +114,12 @@ def main():
to_output=transition_to_robot_action,
)
# Create the dataset
# Create the dataset, deriving features from the pipelines so the on-disk schema
# matches exactly what the pipelines produce at runtime.
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),
@@ -144,7 +142,7 @@ def main():
# Initialize the keyboard listener and rerun visualization
listener, events = init_keyboard_listener()
init_rerun(session_name="recording_phone")
init_rerun(session_name="recording_so100_ee")
try:
if not leader.is_connected or not follower.is_connected:
@@ -160,14 +158,14 @@ def main():
robot=follower,
events=events,
fps=FPS,
teleop_action_processor=leader_joints_to_ee,
robot_action_processor=ee_to_follower_joints,
robot_observation_processor=follower_joints_to_ee,
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,
)
# Reset the environment if not stopping or re-recording
@@ -179,13 +177,13 @@ def main():
robot=follower,
events=events,
fps=FPS,
teleop_action_processor=leader_joints_to_ee,
robot_action_processor=ee_to_follower_joints,
robot_observation_processor=follower_joints_to_ee,
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"]: