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Robot Behavior Adaptation for Human-Robot Interaction based on Policy Gradient Reinforcement Learning

Noriaki Mitsunaga, Christian Smith, Takayuki Kanda, Hiroshi Ishiguro, and Norihiro Hagita.


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.

Keywords: policy gradient reinforcement learning, PGRL, human-robot interaction, adaptive behavior, proxemics.



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