examples/tutorial/pi0/using_pi0_example.py

import torch

from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.datasets.utils import hw_to_dataset_features
from lerobot.policies.factory import make_pre_post_processors
from lerobot.policies.pi0.modeling_pi0 import PI0Policy
from lerobot.policies.utils import build_inference_frame, make_robot_action
from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
from lerobot.robots.so100_follower.so100_follower import SO100Follower

MAX_EPISODES = 5
MAX_STEPS_PER_EPISODE = 20

device = torch.device("mps")  # or "cuda" or "cpu"
model_id = "lerobot/pi0_base"

model = PI0Policy.from_pretrained(model_id)

preprocess, postprocess = make_pre_post_processors(
    model.config,
    model_id,
    # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
    preprocessor_overrides={"device_processor": {"device": str(device)}},
)

# 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 and environment configuration
# Camera keys must match the name and resolutions of the ones used for training!
# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
camera_config = {
    "base_0_rgb": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
    "left_wrist_0_rgb": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
    "right_wrist_0_rgb": OpenCVCameraConfig(index_or_path=2, width=640, height=480, fps=30),
}

robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
robot = SO100Follower(robot_cfg)
robot.connect()

task = ""  # something like "pick the red block"
robot_type = ""  # something like "so100_follower" for multi-embodiment datasets

# This is used to match the raw observation keys to the keys expected by the policy
action_features = hw_to_dataset_features(robot.action_features, "action")
obs_features = hw_to_dataset_features(robot.observation_features, "observation")
dataset_features = {**action_features, **obs_features}

for _ in range(MAX_EPISODES):
    for _ in range(MAX_STEPS_PER_EPISODE):
        obs = robot.get_observation()
        obs_frame = build_inference_frame(
            observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
        )

        obs = preprocess(obs_frame)

        action = model.select_action(obs)
        action = postprocess(action)
        action = make_robot_action(action, dataset_features)
        robot.send_action(action)

    print("Episode finished! Starting new episode...")