摘要This paper presents a learning-based control framework for fast(＜1.5 s)and accurate manipulation of a flexible 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 flexible object.