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

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5 Author(s)
Mitsunaga, N. ; ATR Intelligent Robotics & Commun. Labs., Japan ; Smith, C. ; Kanda, T. ; Ishiguro, H.
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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.

Published in:

Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on

Date of Conference:

2-6 Aug. 2005