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

  • Mitsunaga Noriaki
    Advanced Telecommunications Research Institute International
  • Smith Christian
    Advanced Telecommunications Research Institute International Royal Institute of Technology, Stockholm
  • Kanda Takayuki
    Advanced Telecommunications Research Institute International
  • Ishiguro Hiroshi
    Advanced Telecommunications Research Institute International Graduate School of Engineering, Osaka University
  • Hagita Norihiro
    Advanced Telecommunications Research Institute International

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Other Title
  • 方策勾配型強化学習によるロボットの対人行動の個人適応
  • ホウサク コウバイガタ キョウカ ガクシュウ ニ ヨル ロボット ノ タイジン コウドウ ノ コジン テキオウ

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Abstract

When humans interact in a social context, there are many factors apart from the actual communication that need to be considered. Previous studies in behavioral sciences have shown that there is a need for a certain amount of personal space and that different people tend to meet the gaze of others to different extents. For humans, this is mostly subconscious, but when two persons interact, there is an automatic adjustment of these factors to avoid discomfort. In this paper we propose an adaptation mechanism for robot behaviors to make human-robot interactions run more smoothly. We propose such a mechanism based on policy gradient reinforcement learning, that 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 the experiment with twelve subjects.

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