Adaptation of an Interactive Robot's Behavior Using Policy Gradient Reinforcement Learning
2005 (English)In: Proceedings of the 10th Robotics Symposia, 2005, 319-324 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
2005. 319-324 p.
policy gradient reinforcement learning, PGRL, human-robot interaction, adaptive behavior, proxemics.
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-38202OAI: oai:DiVA.org:kth-38202DiVA: diva2:436243
10th Robotics Symposia