Method for robot assembly skill learning

Abstract

A method and system for robot skill learning applicable to high precision assembly tasks employing a compliance controller. An actor-critic reinforcement learning controller is coupled to the compliance controller, where an actor neural network provides target position adjustment action data to the compliance controller based on robot state data feedback, and a critic neural network is used to train the actor. The critic neural network receives the robot state data feedback and reward data from the robot, along with the action data from the actor, and correlates optimal actions associated with states in order to maximize the reward. The critic then adjusts the parameters of the actor so that the actor produces effective actions in response to the state data, leading to rapid and reliable completion of the assembly task by the compliance controller/robot system. The critic is no longer used after the actor is adequately trained.