Fuzzy reinforcement compliance control for robotic assembly

Compliance inherently involves modification of the robot trajectory based on the contact forces occurring during the motion and enables the robot to perform a variety of manipulation tasks which require fine motion skills. Learning of active compliance behavior can endow a robot with some form of autonomous intelligence which can be very useful for the control of manipulators working in a partially known environment and for manufacturing automation. This paper reports on the acquisition of robot fine motion skills by means of learning a compliance control strategy using fuzzy reinforcement learning. The fuzzy reinforcement compliance controller is applied to a typical robotic assembly task and its performance is compared with other learning controllers.