Forward and inverse kinematics models are fundamental to robot arms, serving as the basis for the robot arm's operational tasks. However, in model learning of robot arms, especially in the presence of redundant degrees of freedom, inverse model learning is more challenging than forward model learning due to the non-convex problem caused by multiple solutions. In this paper, we propose a framework for autonomous learning of the robot arm inverse model based on embodied self-supervised learning (EMSSL) with sampling and training coordination. We investigate batch inference and parallel computation strategies for data sampling in order to accelerate model learning and propose two approaches for fast adaptation of the robot arm model. A series of experiments demonstrate the effectiveness of the method we proposed. The related code will be available soon.

翻译：正运动学与逆运动学模型是机械臂的基础模型，为机械臂的操作任务提供支撑。然而，在机械臂模型学习过程中，尤其是存在冗余自由度时，由于多解导致的非凸问题，逆模型学习比正模型学习更具挑战性。本文提出了一种基于采样与训练协同的具身自监督学习（EMSSL）框架，用于机械臂逆模型的自主学习。我们研究了数据采样中的批量推理与并行计算策略以加速模型学习，并提出了两种机械臂模型快速自适应方法。系列实验验证了所提方法的有效性。相关代码即将公开。