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import threading
from lerobot.async_inference.configs import RobotClientConfig
from lerobot.async_inference.helpers import visualize_action_queue_size
from lerobot.async_inference.robot_client import RobotClient
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.robots.so100_follower import SO100FollowerConfig
# these cameras must match the ones expected by the policy - find your cameras with lerobot-find-cameras
# check the config.json on the Hub for the policy you are using to see the expected camera specs
camera_cfg = {
"up": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
"side": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
}
# # find ports using lerobot-find-port
follower_port = ... # something like "/dev/tty.usbmodem58760431631"
# # the robot ids are used the load the right calibration files
follower_id = ... # something like "follower_so100"
robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_cfg)
server_address = ... # something like "127.0.0.1:8080" if using localhost
# 3. Create client configuration
client_cfg = RobotClientConfig(
robot=robot_cfg,
server_address=server_address,
policy_device="mps",
policy_type="act",
pretrained_name_or_path="fracapuano/robot_learning_tutorial_act",
chunk_size_threshold=0.5, # g
actions_per_chunk=50, # make sure this is less than the max actions of the policy
)
# 4. Create and start client
client = RobotClient(client_cfg)
# 5. Provide a textual description of the task
task = ...
if client.start():
# Start action receiver thread
action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
action_receiver_thread.start()
try:
# Run the control loop
client.control_loop(task)
except KeyboardInterrupt:
client.stop()
action_receiver_thread.join()
# (Optionally) plot the action queue size
visualize_action_queue_size(client.action_queue_size)
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