Touching Motion with Deep Reinforcement Learning Based Force Control

Abstract

In this paper, a deep Q-learning is applied to realize a force control for a touching motion. The impedance control is generally used in case that a robot contacts to an environment. However, the robot keeps bouncing on the surface of the touching object, if the approaching speed is not slow enough. Therefore, we attempted to realize a higher approaching speed without a bouncing and developed a novel force controller based on a deep Q-learning. This controller decides the velocity command values based on the force value acting on the robot, the velocity of the robot and the past velocity commands. The controller was tested by an experiment. A performance exceeding an impedance control was realized after the 19840 trials.