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| import numpy as np |
| import torch |
| from dataclasses import dataclass |
| from typing import Dict |
|
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|
| @dataclass |
| class JointsAbsPosition: |
| joints_pos: torch.Tensor |
| """Joint positions in radians""" |
|
|
| joints_order_config: Dict[str, int] |
| """Joints order configuration""" |
|
|
| device: torch.device |
| """Device to store the tensor on""" |
|
|
| @staticmethod |
| def zero(joint_order_config: Dict[str, int], device: torch.device): |
| return JointsAbsPosition( |
| joints_pos=torch.zeros((len(joint_order_config)), device=device), |
| joints_order_config=joint_order_config, |
| device=device, |
| ) |
|
|
| def to_array(self) -> torch.Tensor: |
| return self.joints_pos.cpu().numpy() |
|
|
| @staticmethod |
| def from_array(array: np.ndarray, joint_order_config: Dict[str, int], device: torch.device) -> "JointsAbsPosition": |
| return JointsAbsPosition( |
| joints_pos=torch.from_numpy(array).to(device), joints_order_config=joint_order_config, device=device |
| ) |
|
|
| def set_joints_pos(self, joints_pos: torch.Tensor): |
| self.joints_pos = joints_pos.to(self.device) |
|
|
| def get_joints_pos(self, device: torch.device = None) -> torch.Tensor: |
| if device is None: |
| return self.joints_pos |
| else: |
| return self.joints_pos.to(device) |
|
|
