Human beings subconsciously adapt their behaviors to a communication partner in order to make interactions run smoothly. In human-robot interactions, not only the human but also the robot is expected to adapt to its partner. Thus, to facilitate human-robot interactions, a robot should be able to read subconscious comfort and discomfort signals from humans and adjust its behavior accordingly, just like a human would. However, most previous, research works expected the human to consciously give feedback, which might interfere with the aim of interaction. We propose an adaptation mechanism based on reinforcement learning that reads subconscious body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interactions. The mechanism uses gazing at the robot's face and human movement distance as subconscious body signals that indicate a human's comfort and discomfort. A pilot study with a humanoid robot that has ten interaction behaviors has been conducted. The study result of 12 subjects suggests that the proposed mechanism enables autonomous adaptation to individual preferences. Also, detailed discussion and conclusions are presented. ; © 2008 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.QC 20110711
Adapting robot behavior for human-robot interaction
2008-01-01
Scopus 2-s2.0-50649121596
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
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