Robot Behavior Adaptation for Human-Robot Interaction based on Policy Gradient Reinforcement Learning
2005 (English)In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005)., IEEE , 2005, 1594-1601 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2005. 1594-1601 p.
policy gradient reinforcement learning (PGRL), human-robot interaction, behavior adaptation, proxemics
IdentifiersURN: urn:nbn:se:kth:diva-38199DOI: 10.1109/IROS.2005.1545206ISI: 000235632101092ScopusID: 2-s2.0-34250186688ISBN: 0-7803-8912-3OAI: oai:DiVA.org:kth-38199DiVA: diva2:436245
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