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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.12718 |
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| _version_ | 1866915253537734656 |
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| author | Weiming, Qu Tianlin, Liu Jiawei, Du Dingsheng, Luo |
| author_facet | Weiming, Qu Tianlin, Liu Jiawei, Du Dingsheng, Luo |
| contents | In the field of signal processing for robotics, the inverse kinematics of robot arms presents a significant challenge due to multiple solutions caused by redundant degrees of freedom (DOFs). Precision is also a crucial performance indicator for robot arms. Current methods typically rely on conditional deep generative models (CDGMs), which often fall short in precision. In this paper, we propose Conditional Embodied Self-Supervised Learning (CEMSSL) and introduce a unified framework based on CEMSSL for high-precision multi-solution inverse kinematics learning. This framework enhances the precision of existing CDGMs by up to 2-3 orders of magnitude while maintaining their original properties. Furthermore, our method is extendable to other fields of signal processing where obtaining multi-solution data in advance is challenging, as well as to other problems involving multi-solution inverse processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_12718 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CEMSSL: Conditional Embodied Self-Supervised Learning is All You Need for High-precision Multi-solution Inverse Kinematics of Robot Arms Weiming, Qu Tianlin, Liu Jiawei, Du Dingsheng, Luo Robotics In the field of signal processing for robotics, the inverse kinematics of robot arms presents a significant challenge due to multiple solutions caused by redundant degrees of freedom (DOFs). Precision is also a crucial performance indicator for robot arms. Current methods typically rely on conditional deep generative models (CDGMs), which often fall short in precision. In this paper, we propose Conditional Embodied Self-Supervised Learning (CEMSSL) and introduce a unified framework based on CEMSSL for high-precision multi-solution inverse kinematics learning. This framework enhances the precision of existing CDGMs by up to 2-3 orders of magnitude while maintaining their original properties. Furthermore, our method is extendable to other fields of signal processing where obtaining multi-solution data in advance is challenging, as well as to other problems involving multi-solution inverse processes. |
| title | CEMSSL: Conditional Embodied Self-Supervised Learning is All You Need for High-precision Multi-solution Inverse Kinematics of Robot Arms |
| topic | Robotics |
| url | https://arxiv.org/abs/2306.12718 |