Reinforcement learning and robust control for robot compliance tasks

The complexity in planning and control of robot compliance tasks mainly res ults from simultaneous control of both position and force and inevitable co ntact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. I n addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we pr opose a reinforcement learning and robust control scheme for robot complian ce tasks. A reinforcement learning mechanism is used to tackle variations a mong compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the rei nforcement learning mechanism. Simulations based on deburring compliance ta sks demonstrate the effectiveness of the proposed scheme.