Robot behavior adaptation for human-robot interaction based on policy gradient reinforcement learning

An adaptation mechanism for robot behaviors to make robot-human interactions run more smoothly is proposed based on reinforcement learning, which reads minute body signals from a human partner and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interaction.

Abstract

In this paper we propose an adaptation mechanism for robot behaviors to make robot-human interactions run more smoothly. We propose such a mechanism based on reinforcement learning, which reads minute body signals from a human partner, and uses this information to adjust interaction distances, gaze-meeting, and motion speed and timing in human-robot interaction. We show that this enables autonomous adaptation to individual preferences by an experiment with twelve subjects.