Multiple solutions mainly originate from the existence of redundant degrees of freedom in the robot arm, which may cause difficulties in inverse model learning but they can also bring many benefits, such as higher flexibility and robustness. Current multi-solution inverse model learning methods rely on conditional deep generative models, yet they often fail to achieve sufficient precision when learning multiple solutions. In this paper, we propose Conditional Embodied Self-Supervised Learning (CEMSSL) for robot arm multi-solution inverse model learning, and present a unified framework for high-precision multi-solution inverse model learning that is applicable to other conditional deep generative models. Our experimental results demonstrate that our framework can achieve a significant improvement in precision (up to 2 orders of magnitude) while preserving the properties of the original method. The related code will be available soon.

翻译：多解主要源于机械臂冗余自由度的存在，这可能给逆模型学习带来困难，但也能带来诸如更高灵活性和鲁棒性等许多优势。当前的多解逆模型学习方法依赖于条件深度生成模型，然而在学习多解时往往难以达到足够的精度。本文提出条件化具身自监督学习（CEMSSL）用于机械臂多解逆模型学习，并呈现一个适用于其他条件深度生成模型的高精度多解逆模型学习统一框架。实验结果表明，我们的框架在保持原始方法特性的同时，能够实现精度的大幅提升（高达两个数量级）。相关代码即将公开。