Adaptation of an Interactive Robot's Behavior Using Policy Gradient Reinforcement Learning
Christian Smith, Noriaki Mitsunaga, Takayuki Kanda, Hiroshi Ishiguro, Norihiro Hagita · 2005
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.