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https://github.com/huggingface/lerobot.git
synced 2026-05-22 20:19:43 +00:00
fix(examples): wrap all of them into a main function (#2524)
This commit is contained in:
Steven Palma
+142
-133
@@ -50,133 +50,122 @@ RESET_TIME_SEC = 30
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TASK_DESCRIPTION = "My task description"
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HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
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# Create the robot and teleoperator configurations
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camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
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robot_config = SO100FollowerConfig(
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port="/dev/tty.usbmodem5A460814411",
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id="my_awesome_follower_arm",
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cameras=camera_config,
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use_degrees=True,
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)
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teleop_config = PhoneConfig(phone_os=PhoneOS.IOS) # or PhoneOS.ANDROID
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# Initialize the robot and teleoperator
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robot = SO100Follower(robot_config)
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phone = Phone(teleop_config)
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def main():
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# Create the robot and teleoperator configurations
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camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
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robot_config = SO100FollowerConfig(
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port="/dev/tty.usbmodem5A460814411",
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id="my_awesome_follower_arm",
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cameras=camera_config,
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use_degrees=True,
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)
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teleop_config = PhoneConfig(phone_os=PhoneOS.IOS) # or PhoneOS.ANDROID
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# 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
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kinematics_solver = RobotKinematics(
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urdf_path="./SO101/so101_new_calib.urdf",
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target_frame_name="gripper_frame_link",
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joint_names=list(robot.bus.motors.keys()),
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)
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# Initialize the robot and teleoperator
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robot = SO100Follower(robot_config)
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phone = Phone(teleop_config)
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# Build pipeline to convert phone action to EE action
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phone_to_robot_ee_pose_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
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steps=[
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MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
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EEReferenceAndDelta(
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kinematics=kinematics_solver,
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end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
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motor_names=list(robot.bus.motors.keys()),
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use_latched_reference=True,
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),
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EEBoundsAndSafety(
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end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
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max_ee_step_m=0.20,
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),
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GripperVelocityToJoint(speed_factor=20.0),
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],
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to_transition=robot_action_observation_to_transition,
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to_output=transition_to_robot_action,
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)
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# Build pipeline to convert EE action to joints action
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robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
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steps=[
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InverseKinematicsEEToJoints(
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kinematics=kinematics_solver,
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motor_names=list(robot.bus.motors.keys()),
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initial_guess_current_joints=True,
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),
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],
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to_transition=robot_action_observation_to_transition,
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to_output=transition_to_robot_action,
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)
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# Build pipeline to convert joint observation to EE observation
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robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
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steps=[
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ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
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],
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to_transition=observation_to_transition,
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to_output=transition_to_observation,
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)
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# Create the dataset
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dataset = LeRobotDataset.create(
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repo_id=HF_REPO_ID,
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fps=FPS,
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features=combine_feature_dicts(
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# Run the feature contract of the pipelines
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# This tells you how the features would look like after the pipeline steps
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aggregate_pipeline_dataset_features(
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pipeline=phone_to_robot_ee_pose_processor,
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initial_features=create_initial_features(action=phone.action_features),
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use_videos=True,
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),
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aggregate_pipeline_dataset_features(
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pipeline=robot_joints_to_ee_pose,
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initial_features=create_initial_features(observation=robot.observation_features),
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use_videos=True,
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),
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),
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robot_type=robot.name,
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use_videos=True,
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image_writer_threads=4,
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)
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# Connect the robot and teleoperator
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robot.connect()
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phone.connect()
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# Initialize the keyboard listener and rerun visualization
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listener, events = init_keyboard_listener()
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init_rerun(session_name="phone_so100_record")
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if not robot.is_connected or not phone.is_connected:
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raise ValueError("Robot or teleop is not connected!")
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print("Starting record loop. Move your phone to teleoperate the robot...")
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episode_idx = 0
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while episode_idx < NUM_EPISODES and not events["stop_recording"]:
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log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
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# Main record loop
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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teleop=phone,
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dataset=dataset,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=phone_to_robot_ee_pose_processor,
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robot_action_processor=robot_ee_to_joints_processor,
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robot_observation_processor=robot_joints_to_ee_pose,
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# 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
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kinematics_solver = RobotKinematics(
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urdf_path="./SO101/so101_new_calib.urdf",
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target_frame_name="gripper_frame_link",
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joint_names=list(robot.bus.motors.keys()),
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)
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# Reset the environment if not stopping or re-recording
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if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
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log_say("Reset the environment")
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# Build pipeline to convert phone action to EE action
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phone_to_robot_ee_pose_processor = RobotProcessorPipeline[
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tuple[RobotAction, RobotObservation], RobotAction
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](
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steps=[
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MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
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EEReferenceAndDelta(
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kinematics=kinematics_solver,
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end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
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motor_names=list(robot.bus.motors.keys()),
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use_latched_reference=True,
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),
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EEBoundsAndSafety(
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end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
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max_ee_step_m=0.20,
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),
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GripperVelocityToJoint(speed_factor=20.0),
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],
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to_transition=robot_action_observation_to_transition,
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to_output=transition_to_robot_action,
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)
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# Build pipeline to convert EE action to joints action
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robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
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steps=[
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InverseKinematicsEEToJoints(
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kinematics=kinematics_solver,
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motor_names=list(robot.bus.motors.keys()),
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initial_guess_current_joints=True,
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),
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],
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to_transition=robot_action_observation_to_transition,
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to_output=transition_to_robot_action,
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)
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# Build pipeline to convert joint observation to EE observation
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robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
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steps=[
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ForwardKinematicsJointsToEE(
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kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
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)
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],
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to_transition=observation_to_transition,
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to_output=transition_to_observation,
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)
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# Create the dataset
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dataset = LeRobotDataset.create(
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repo_id=HF_REPO_ID,
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fps=FPS,
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features=combine_feature_dicts(
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# Run the feature contract of the pipelines
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# This tells you how the features would look like after the pipeline steps
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aggregate_pipeline_dataset_features(
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pipeline=phone_to_robot_ee_pose_processor,
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initial_features=create_initial_features(action=phone.action_features),
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use_videos=True,
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),
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aggregate_pipeline_dataset_features(
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pipeline=robot_joints_to_ee_pose,
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initial_features=create_initial_features(observation=robot.observation_features),
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use_videos=True,
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),
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),
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robot_type=robot.name,
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use_videos=True,
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image_writer_threads=4,
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)
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# Connect the robot and teleoperator
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robot.connect()
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phone.connect()
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# Initialize the keyboard listener and rerun visualization
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listener, events = init_keyboard_listener()
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init_rerun(session_name="phone_so100_record")
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if not robot.is_connected or not phone.is_connected:
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raise ValueError("Robot or teleop is not connected!")
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print("Starting record loop. Move your phone to teleoperate the robot...")
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episode_idx = 0
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while episode_idx < NUM_EPISODES and not events["stop_recording"]:
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log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
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# Main record loop
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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teleop=phone,
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control_time_s=RESET_TIME_SEC,
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dataset=dataset,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=phone_to_robot_ee_pose_processor,
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@@ -184,22 +173,42 @@ while episode_idx < NUM_EPISODES and not events["stop_recording"]:
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robot_observation_processor=robot_joints_to_ee_pose,
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)
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if events["rerecord_episode"]:
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log_say("Re-recording episode")
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events["rerecord_episode"] = False
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events["exit_early"] = False
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dataset.clear_episode_buffer()
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continue
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# Reset the environment if not stopping or re-recording
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if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
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log_say("Reset the environment")
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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teleop=phone,
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control_time_s=RESET_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=phone_to_robot_ee_pose_processor,
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robot_action_processor=robot_ee_to_joints_processor,
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robot_observation_processor=robot_joints_to_ee_pose,
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)
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# Save episode
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dataset.save_episode()
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episode_idx += 1
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if events["rerecord_episode"]:
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log_say("Re-recording episode")
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events["rerecord_episode"] = False
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events["exit_early"] = False
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dataset.clear_episode_buffer()
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continue
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# Clean up
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log_say("Stop recording")
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robot.disconnect()
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phone.disconnect()
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listener.stop()
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# Save episode
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dataset.save_episode()
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episode_idx += 1
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dataset.finalize()
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dataset.push_to_hub()
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# Clean up
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log_say("Stop recording")
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robot.disconnect()
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phone.disconnect()
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listener.stop()
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dataset.finalize()
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dataset.push_to_hub()
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if __name__ == "__main__":
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main()
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