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|
| | import numpy as np |
| | import torch |
| | from dataclasses import dataclass |
| | from typing import Dict |
| |
|
| |
|
| | @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) |
| |
|