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

@article{Mitsunaga2005RobotBA,
  title={Robot behavior adaptation for human-robot interaction based on policy gradient reinforcement learning},
  author={Noriaki Mitsunaga and Christian Smith and Takayuki Kanda and Hiroshi Ishiguro and Norihiro Hagita},
  journal={2005 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2005},
  pages={218-225}
}
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. 
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