Robot Behavior Adaptation for Human-Robot Interaction based on Policy Gradient Reinforcement Learning
Robot Behavior Adaptation for Human-Robot Interaction based on Policy Gradient Reinforcement Learning
- New search for: Mitsunaga, Noriaki
- New search for: Smith, Christian
- New search for: Kanda, Takayuki
- New search for: Ishiguro, Hiroshi
- New search for: Hagita, Norihiro
- New search for: Mitsunaga, Noriaki
- New search for: Smith, Christian
- New search for: Kanda, Takayuki
- New search for: Ishiguro, Hiroshi
- New search for: Hagita, Norihiro
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. ; © 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.
Document information
Robot Behavior Adaptation for Human-Robot Interaction based on Policy Gradient Reinforcement Learning
2005-01-01
Conference paper
Electronic Resource
English
| DDC: | 629 |
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