Robot behavior adaptation for human-robot interaction based on policy gradient reinforcement learning

  title={Robot behavior adaptation for human-robot interaction based on policy gradient reinforcement learning},
  author={Noriaki Mitsunaga and Christian Smith and Takayuki Kanda and Hiroshi Ishiguro and Norihiro Hagita},
  journal={2005 IEEE/RSJ International Conference on Intelligent Robots and Systems},
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. 
47 Extracted Citations
11 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 47 extracted citations

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 11 references

Interpersonal relationships and personal space: Research review and theoretical model

  • E. Sundstrom, I. Altman
  • Human Ecology,
  • 1976
Highly Influential
3 Excerpts

Acquisition of Probabilistic Behavior Decision Model based on the Interactive Teaching Method

  • T. Inamura, M. Inaba, H. Inoue
  • In Proc. of the 1999 International Conference on…
  • 1999
1 Excerpt

Research regarding personal distances between people and a moving robot (in Japanese)

  • K. Nakajima
  • PhD thesis, Kyuushuu Institute of Design,
  • 1998
2 Excerpts

Face-to-Face Interaction: Research, Methods, and Theory

  • S. Duncan, D. W. Fiske
  • Lawrence Erlbaum Associates, Inc., Publishers,
  • 1977
2 Excerpts

Similar Papers

Loading similar papers…