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Adaptation of an Interactive Robot's Behavior Using Policy Gradient Reinforcement Learning
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CAS)ORCID iD: 0000-0003-2078-8854
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2005 (English)In: Proceedings of the 10th Robotics Symposia, 2005, 319-324 p.Conference paper, Published 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
2005. 319-324 p.
Keyword [en]
policy gradient reinforcement learning, PGRL, human-robot interaction, adaptive behavior, proxemics.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-38202OAI: oai:DiVA.org:kth-38202DiVA: diva2:436243
Conference
10th Robotics Symposia
Available from: 2011-08-22 Created: 2011-08-22 Last updated: 2011-09-12Bibliographically approved

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Smith, Christian

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