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arXiv · 2306.12718

CEMSSL: Conditional Embodied Self-Supervised Learning is All You Need for High-precision Multi-solution Inverse Kinematics of Robot Arms

Qu Weiming·Liu Tianlin·Du Jiawei·Luo Dingsheng
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Abstract

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.

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Qu Weiming, Liu Tianlin, Du Jiawei, Luo Dingsheng. 2023-06-22. CEMSSL: Conditional Embodied Self-Supervised Learning is All You Need for High-precision Multi-solution Inverse Kinematics of Robot Arms. https://arxiv.org/abs/2306.12718

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