Reinforcement learning control for coordinated manipulation of multi-robots

In this paper, coordination control is investigated for multi-robots to manipulate an object with a common desired trajectory. Both trajectory tracking and control input minimization are considered for each individual robot manipulator, such that possible disagreement between different manipulators can be handled. Reinforcement learning is employed to cope with the problem of unknown dynamics of both robots and the manipulated object. It is rigorously proven that the proposed method guarantees the coordination control of the multi-robots system under study. The validity of the proposed method is verified through simulation studies.