Policy gradient learning of cooperative interaction with a robot using user's biological signals

Abstract

The potential market of robots that can helpfully work at home is increasing, and such robots are required to possess force and tactile sensors achieving dynamic and cooperative interactions with their users. Virtual realization of force/tactile sensors in robots, using user's biological signals such as EMG and postural information, is a versatile solution allowing high spatial resolution and degrees of freedom. In this paper, however, we first show the virtual force sensing approach does not work for a three-dimensional cooperative task in which the user is requested to move a load by an upper-limb of the user cooperatively with the robot, and discuss about inevitable problems. We then propose to apply policy gradient learning to overcome the problems, and demonstrate preliminary but promising learning results. © 2009 Springer Berlin Heidelberg.