forked from tangger/lerobot
d2782cf66b
* chore: replace hard-coded 'action' values with constants throughout all the source code * chore(tests): replace hard-coded action values with constants throughout all the test code
101 lines
3.5 KiB
Python
101 lines
3.5 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 RobotAction, RobotObservation, RobotProcessorPipeline
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from lerobot.processor.converters import (
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robot_action_observation_to_transition,
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transition_to_robot_action,
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)
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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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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.constants import ACTION
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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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# Initialize the robot config
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robot_config = SO100FollowerConfig(
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port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True
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)
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# Initialize the robot
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robot = SO100Follower(robot_config)
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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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# 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=False, # Because replay is open loop
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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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# Fetch the dataset to replay
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dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX])
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# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0
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episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX)
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actions = episode_frames.select_columns(ACTION)
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# Connect to the robot
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robot.connect()
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if not robot.is_connected:
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raise ValueError("Robot is not connected!")
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print("Starting replay loop...")
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log_say(f"Replaying episode {EPISODE_IDX}")
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for idx in range(len(episode_frames)):
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t0 = time.perf_counter()
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# Get recorded action from dataset
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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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# Get robot observation
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robot_obs = robot.get_observation()
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# Dataset EE -> robot joints
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joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
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# Send action to robot
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_ = robot.send_action(joint_action)
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busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0))
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# Clean up
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robot.disconnect()
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