Abstract: This paper presents a learning-based control framework for fast (< 1.5 s) and accurate manipulation of a fexible object, i.e., whip targeting. The framework consists of a motion planner learned or optimized by an algorithm, Online Impedance Adaptation Control (OIAC), a sim2real mechanism, and a visual feedback component. The experimental results show that a soft actor-critic algorithm outperforms three Deep Reinforcement Learning (DRL), a nonlinear optimization, and a genetic algorithm in learning generalization of motion planning. It can greatly reduce average learning trials (to < 20% of others) and maximize average rewards (to > 3 times of others). Besides, motion tracking errors are greatly reduced to 13.29% and 22.36% of constant impedance control by the OIAC of the proposed framework. In addition, the trajectory similarity between simulated and physical whips is 89.09%. The presented framework provides a new method integrating data-driven and physics-based algorithms for controlling fast and accurate arm manipulation of a fexible object.

Key words: Deep reinforcement learning , · Deformable object manipulation , · Variable impedance control , · Sim2real , · Visual tracking