  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.
