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Robot Behavior Adaptation for Human-Robot Interaction based on Policy Gradient Reinforcement Learning
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. (CAS)ORCID iD: 0000-0003-2078-8854
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2005 (English)In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005)., IEEE , 2005, 1594-1601 p.Conference paper (Refereed)
Abstract [en]

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.
Keyword [en]
policy gradient reinforcement learning (PGRL), human-robot interaction, behavior adaptation, proxemics
National Category
URN: urn:nbn:se:kth:diva-38199DOI: 10.1109/IROS.2005.1545206ISI: 000235632101092ScopusID: 2-s2.0-34250186688ISBN: 0-7803-8912-3OAI: diva2:436245
© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Available from: 2011-09-26 Created: 2011-08-22 Last updated: 2011-09-26Bibliographically approved

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