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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.smolvla.modeling_smolvla import SmolVLAPolicy
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/smolvla_base"
model = SmolVLAPolicy.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 = {
"camera1": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
"camera2": OpenCVCameraConfig(index_or_path=1, 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...")
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