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* 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.
139 lines
4.0 KiB
Python
139 lines
4.0 KiB
Python
# Copyright 2024 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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"""
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Replays the actions of an episode from a dataset on a robot.
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Examples:
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```shell
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lerobot-replay \
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--robot.type=so100_follower \
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--robot.port=/dev/tty.usbmodem58760431541 \
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--robot.id=black \
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--dataset.repo_id=aliberts/record-test \
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--dataset.episode=2
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```
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Example replay with bimanual so100:
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```shell
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lerobot-replay \
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--robot.type=bi_so100_follower \
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--robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
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--robot.right_arm_port=/dev/tty.usbmodem5A460812391 \
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--robot.id=bimanual_follower \
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--dataset.repo_id=${HF_USER}/bimanual-so100-handover-cube \
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--dataset.episode=0
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```
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"""
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import logging
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import time
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from dataclasses import asdict, dataclass
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from pathlib import Path
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from pprint import pformat
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from lerobot.configs import parser
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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from lerobot.processor import IdentityProcessorStep, 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 import ( # noqa: F401
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Robot,
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RobotConfig,
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bi_so100_follower,
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hope_jr,
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koch_follower,
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make_robot_from_config,
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reachy2,
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so100_follower,
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so101_follower,
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)
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from lerobot.utils.robot_utils import busy_wait
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from lerobot.utils.utils import (
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init_logging,
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log_say,
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)
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@dataclass
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class DatasetReplayConfig:
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# Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
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repo_id: str
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# Episode to replay.
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episode: int
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# Root directory where the dataset will be stored (e.g. 'dataset/path').
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root: str | Path | None = None
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# Limit the frames per second. By default, uses the policy fps.
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fps: int = 30
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@dataclass
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class ReplayConfig:
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robot: RobotConfig
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dataset: DatasetReplayConfig
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# Use vocal synthesis to read events.
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play_sounds: bool = True
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# Optional processor for actions before sending to robot
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robot_action_processor: RobotProcessorPipeline[RobotAction, RobotAction] | None = None
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@parser.wrap()
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def replay(cfg: ReplayConfig):
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init_logging()
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logging.info(pformat(asdict(cfg)))
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# Initialize robot action processor with default if not provided
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robot_action_processor = cfg.robot_action_processor or RobotProcessorPipeline[RobotAction, RobotAction](
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steps=[IdentityProcessorStep()],
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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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# Reset processor
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robot_action_processor.reset()
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robot = make_robot_from_config(cfg.robot)
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dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode])
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actions = dataset.hf_dataset.select_columns("action")
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robot.connect()
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log_say("Replaying episode", cfg.play_sounds, blocking=True)
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for idx in range(dataset.num_frames):
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start_episode_t = time.perf_counter()
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action_array = actions[idx]["action"]
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action = {}
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for i, name in enumerate(dataset.features["action"]["names"]):
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action[name] = action_array[i]
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# Process action through robot action processor
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processed_action = robot_action_processor(action)
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robot.send_action(processed_action) # type: ignore[arg-type]
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dt_s = time.perf_counter() - start_episode_t
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busy_wait(1 / dataset.fps - dt_s)
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robot.disconnect()
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def main():
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replay()
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if __name__ == "__main__":
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main()
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