| from lerobot.common.datasets.utils import build_dataset_frame, hw_to_dataset_features | |
| from lerobot.common.policies.act.modeling_act import ACTPolicy | |
| from lerobot.common.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig | |
| from lerobot.common.utils.control_utils import predict_action | |
| from lerobot.common.utils.utils import get_safe_torch_device | |
| NB_CYCLES_CLIENT_CONNECTION = 1000 | |
| robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi") | |
| robot = LeKiwiClient(robot_config) | |
| robot.connect() | |
| policy = ACTPolicy.from_pretrained("pepijn223/act_lekiwi_circle") | |
| policy.reset() | |
| obs_features = hw_to_dataset_features(robot.observation_features, "observation") | |
| print("Running inference") | |
| i = 0 | |
| while i < NB_CYCLES_CLIENT_CONNECTION: | |
| obs = robot.get_observation() | |
| observation_frame = build_dataset_frame(obs_features, obs, prefix="observation") | |
| action_values = predict_action( | |
| observation_frame, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp | |
| ) | |
| action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)} | |
| robot.send_action(action) | |
| i += 1 | |
| robot.disconnect() | |