mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-18 10:10:08 +00:00
Adil Zouitine
376a6457cf
* refactor(processor): enhance type annotations for processors in record, replay, teleoperate, and control utils - Updated type annotations for preprocessor and postprocessor parameters in record_loop and predict_action functions to specify the expected dictionary types. - Adjusted robot_action_processor type in ReplayConfig and TeleoperateConfig to improve clarity and maintainability. - Ensured consistency in type definitions across multiple files, enhancing overall code readability. * refactor(processor): enhance type annotations for RobotProcessorPipeline in various files - Updated type annotations for RobotProcessorPipeline instances in evaluate.py, record.py, replay.py, teleoperate.py, and other related files to specify input and output types more clearly. - Introduced new type conversions for PolicyAction and EnvTransition to improve type safety and maintainability across the processing pipelines. - Ensured consistency in type definitions, enhancing overall code readability and reducing potential runtime errors. * refactor(processor): update transition handling in processors to use transition_to_batch - Replaced direct transition handling with transition_to_batch in various processor tests and implementations to ensure consistent batching of input data. - Updated assertions in tests to reflect changes in data structure, enhancing clarity and maintainability. - Improved overall code readability by standardizing the way transitions are processed across different processor types. * refactor(tests): standardize transition key usage in processor tests - Updated assertions in processor test files to utilize the TransitionKey for action references, enhancing consistency across tests. - Replaced direct string references with TransitionKey constants for improved readability and maintainability. - Ensured that all relevant tests reflect these changes, contributing to a more uniform approach in handling transitions.
83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
# !/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import time
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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from lerobot.model.kinematics import RobotKinematics
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from lerobot.processor import RobotProcessorPipeline
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from lerobot.processor.converters import robot_action_to_transition, transition_to_robot_action
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from lerobot.processor.core import RobotAction
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from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
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from lerobot.robots.so100_follower.robot_kinematic_processor import (
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AddRobotObservationAsComplimentaryData,
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InverseKinematicsEEToJoints,
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)
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from lerobot.robots.so100_follower.so100_follower import SO100Follower
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from lerobot.utils.robot_utils import busy_wait
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from lerobot.utils.utils import log_say
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EPISODE_IDX = 0
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HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
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robot_config = SO100FollowerConfig(
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port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", use_degrees=True
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)
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robot = SO100Follower(robot_config)
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robot.connect()
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dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX])
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actions = dataset.hf_dataset.select_columns("action")
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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="./src/lerobot/teleoperators/sim/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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# Build pipeline to convert ee pose action to joint action
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robot_ee_to_joints_processor = RobotProcessorPipeline[RobotAction, RobotAction](
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steps=[
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AddRobotObservationAsComplimentaryData(robot=robot),
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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=False, # Because replay is open loop
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),
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],
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to_transition=robot_action_to_transition,
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to_output=transition_to_robot_action,
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)
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robot_ee_to_joints_processor.reset()
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log_say(f"Replaying episode {EPISODE_IDX}")
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for idx in range(dataset.num_frames):
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t0 = time.perf_counter()
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ee_action = {
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name: float(actions[idx]["action"][i]) for i, name in enumerate(dataset.features["action"]["names"])
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}
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joint_action = robot_ee_to_joints_processor(ee_action)
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action_sent = robot.send_action(joint_action)
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busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
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robot.disconnect()
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