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42d9709 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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)
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