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

TL;DR

This paper introduces CEMSSL, a novel self-supervised learning framework that significantly improves the precision of multi-solution inverse kinematics for robot arms, outperforming existing models by 2-3 orders of magnitude.

Contribution

The paper presents a unified CEMSSL framework that enhances the precision of conditional deep generative models for inverse kinematics, with broad applicability to multi-solution inverse problems.

Findings

Precision improved by 2-3 orders of magnitude

Framework maintains properties of original models

Extendable to other signal processing fields

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.