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# !/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.configs.types import FeatureType, PolicyFeature
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
from lerobot.datasets.utils import combine_feature_dicts
from lerobot.model.kinematics import RobotKinematics
from lerobot.policies.act.modeling_act import ACTPolicy
from lerobot.policies.factory import make_pre_post_processors
from lerobot.processor import (
RobotAction,
RobotObservation,
RobotProcessorPipeline,
make_default_teleop_action_processor,
)
from lerobot.processor.converters import (
observation_to_transition,
robot_action_observation_to_transition,
transition_to_observation,
transition_to_robot_action,
)
from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
from lerobot.robots.so100_follower.robot_kinematic_processor import (
ForwardKinematicsJointsToEE,
InverseKinematicsEEToJoints,
)
from lerobot.robots.so100_follower.so100_follower import SO100Follower
from lerobot.scripts.lerobot_record import record_loop
from lerobot.utils.control_utils import init_keyboard_listener
from lerobot.utils.utils import log_say
from lerobot.utils.visualization_utils import init_rerun
NUM_EPISODES = 5
FPS = 30
EPISODE_TIME_SEC = 60
TASK_DESCRIPTION = "My task description"
HF_MODEL_ID = "<hf_username>/<model_repo_id>"
HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
# Create the robot configuration & robot
camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
robot_config = SO100FollowerConfig(
port="/dev/tty.usbmodem5A460814411",
id="my_awesome_follower_arm",
cameras=camera_config,
use_degrees=True,
)
robot = SO100Follower(robot_config)
# Create policy
policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
# 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
kinematics_solver = RobotKinematics(
urdf_path="./SO101/so101_new_calib.urdf",
target_frame_name="gripper_frame_link",
joint_names=list(robot.bus.motors.keys()),
)
# Build pipeline to convert EE action to joints action
robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
steps=[
InverseKinematicsEEToJoints(
kinematics=kinematics_solver,
motor_names=list(robot.bus.motors.keys()),
initial_guess_current_joints=True,
),
],
to_transition=robot_action_observation_to_transition,
to_output=transition_to_robot_action,
)
# Build pipeline to convert joints observation to EE observation
robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
steps=[
ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
],
to_transition=observation_to_transition,
to_output=transition_to_observation,
)
# Create the dataset
dataset = LeRobotDataset.create(
repo_id=HF_DATASET_ID,
fps=FPS,
features=combine_feature_dicts(
aggregate_pipeline_dataset_features(
pipeline=robot_joints_to_ee_pose_processor,
initial_features=create_initial_features(observation=robot.observation_features),
use_videos=True,
),
# User for now should be explicit on the feature keys that were used for record
# Alternatively, the user can pass the processor step that has the right features
aggregate_pipeline_dataset_features(
pipeline=make_default_teleop_action_processor(),
initial_features=create_initial_features(
action={
f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
}
),
use_videos=True,
),
),
robot_type=robot.name,
use_videos=True,
image_writer_threads=4,
)
# Build Policy Processors
preprocessor, postprocessor = make_pre_post_processors(
policy_cfg=policy,
pretrained_path=HF_MODEL_ID,
dataset_stats=dataset.meta.stats,
# The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
)
# Connect the robot and teleoperator
robot.connect()
# Initialize the keyboard listener and rerun visualization
listener, events = init_keyboard_listener()
init_rerun(session_name="so100_so100_evaluate")
if not robot.is_connected:
raise ValueError("Robot is not connected!")
print("Starting evaluate loop...")
episode_idx = 0
for episode_idx in range(NUM_EPISODES):
log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
# Main record loop
record_loop(
robot=robot,
events=events,
fps=FPS,
policy=policy,
preprocessor=preprocessor, # Pass the pre and post policy processors
postprocessor=postprocessor,
dataset=dataset,
control_time_s=EPISODE_TIME_SEC,
single_task=TASK_DESCRIPTION,
display_data=True,
teleop_action_processor=make_default_teleop_action_processor(),
robot_action_processor=robot_ee_to_joints_processor,
robot_observation_processor=robot_joints_to_ee_pose_processor,
)
# Reset the environment if not stopping or re-recording
if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
log_say("Reset the environment")
record_loop(
robot=robot,
events=events,
fps=FPS,
control_time_s=EPISODE_TIME_SEC,
single_task=TASK_DESCRIPTION,
display_data=True,
teleop_action_processor=make_default_teleop_action_processor(),
robot_action_processor=robot_ee_to_joints_processor,
robot_observation_processor=robot_joints_to_ee_pose_processor,
)
if events["rerecord_episode"]:
log_say("Re-record episode")
events["rerecord_episode"] = False
events["exit_early"] = False
dataset.clear_episode_buffer()
continue
# Save episode
dataset.save_episode()
episode_idx += 1
# Clean up
log_say("Stop recording")
robot.disconnect()
listener.stop()
dataset.finalize()
dataset.push_to_hub()
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