b418409b24
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import torch
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from lerobot.common.policies.act.modeling_act import ACTPolicy
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from lerobot.common.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
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from lerobot.common.robots.lekiwi.lekiwi_client import LeKiwiClient
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from lerobot.common.utils.control_utils import predict_action
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from lerobot.common.utils.utils import get_safe_torch_device
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NB_CYCLES_CLIENT_CONNECTION = 1000
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robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
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robot = LeKiwiClient(robot_config)
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robot.connect()
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policy = ACTPolicy.from_pretrained("pepijn223/act_lekiwi_circle")
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policy.reset()
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print("Running inference")
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i = 0
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while i < NB_CYCLES_CLIENT_CONNECTION:
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obs = robot.get_observation()
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for key, value in obs.items():
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if isinstance(value, torch.Tensor):
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obs[key] = value.numpy()
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action_values = predict_action(
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obs, policy, get_safe_torch_device(policy.config.device), policy.config.use_amp
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)
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action = {
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key: action_values[i].item() if isinstance(action_values[i], torch.Tensor) else action_values[i]
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for i, key in enumerate(robot.action_features)
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}
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robot.send_action(action)
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i += 1
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robot.disconnect()
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