Adapting Multi-Robot Behavior to Communication Atmosphere in Humans-Robots Interaction Using Fuzzy Production Rule Based Friend-Q Learning
Lue-Feng Chen*, Zhen-Tao Liu*,***, Fang-Yan Dong*,
Yoichi Yamazaki**, Min Wu***, and Kaoru Hirota*
*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan
**Department of Electrical, Electronic, and Information Engineering, Kanto Gakuin University, 1-50-1 Mutsuura-higashi, Kanazawa-ku, Yokohama, Kanagawa 236-8501, Japan
***School of Information Science and Engineering, Central South University, Yuelu Mountain, Changsha, Hunan 410083, China
A behavior adaptation mechanism in humans-robots interaction is proposed to adjust robots’ behavior to communication atmosphere, where fuzzy production rule based friend-Q learning (FPRFQ) is introduced. It aims to shorten the response time of robots and decrease the social distance between humans and robots to realize the smooth communication of robots and humans. Experiments on robots/humans interaction are performed in a virtual communication atmosphere environment. Results show that robots adapt well by saving 44 and 482 learning steps compared to that by friend-Q learning (FQ) and independent learning (IL), respectively; additionally, the distance between human-generated atmosphere and robot-generated atmosphere is 3 times and 10 times shorter than the FQ and the IL, respectively. The proposed behavior adaptation mechanism is also applied to robots’ eye movement in the developing humans-robots interaction system, calledmascot robot system, and basic experimental results are shown in home party scenario with five eye robots and four humans.
Yoichi Yamazaki, Min Wu, and Kaoru Hirota, “Adapting Multi-Robot Behavior to Communication Atmosphere in Humans-Robots Interaction Using Fuzzy Production Rule Based Friend-Q Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.2, pp. 291-301, 2013.
-  T. Fong, I. Nourbakhsh, and K. Dautenhahn, “A Survey of Socially Interactive Robots,” Robotics and Autonomous System, Vol.42, No.3, pp. 143-166, 2003.
-  S. Ikemoto, H. B. Amor, T. Minato, H. Ishiguro, and B. Jung, “Physical Interaction Learning: Behavior Adaptation in Cooperative Human-Robot Tasks Involving Physical Contact,” IEEE Int. Symposium on Robot and Human Interative Communication, Toyama, Japan, pp. 504-509, 2009.
-  J. Chan and G. Nejat, “A Learning-based Control Architecture for an Assistive Robot Providing Social Engagement during Cognitively Stimulating Activities,” IEEE Int. Conf. on Robotics and Automation, Shanghai, China, pp. 3928-3933, 2011.
-  M. Malfaz and M. A. Salichs, “Learning to Deal with Objects,” IEEE Int. Symposium on Robot and Human Interactive Communication, Toyama, Japan, pp. 504-509, 2001.
-  C. J. H. Watkins and P. Dayan, “Q-learning,” Machine Learning, Vol.8, No.3-4, pp. 279-292, 1992.
-  M. L. Littman, “Friend-or-Foe Q-learning in General-Sum Games,” Proc. of 18th Int. Conf. on Machine Learning, Massachusetts, America, pp. 322-328, 2001.
-  M. Tan, “Muti-Agent Reinforcement Learning: Independent vs. Cooperative Agents,” Proc. of 10th Int. Conf. on Machine Learning, Massachusetts, America, pp. 330-337, 1993.
-  N. Mitsunaga, C. Smith, T. Kanda, H. Ishiguro, and N. Hagita, “Adapting Robot Behavior for Human-Robot Interaction,” IEEE Trans. on Robotics, Vol.24, No.4, pp. 911-916, 2008.
-  R. Cantrell, P. Schermerhorn, and M. Scheutz, “Learning Actions from Human-Robot Dialogues,” IEEE Int. Symposium on Robot and Human Interactive Communication, Atlanta, America, pp. 125-130, 2011.
-  M. A. Salichs and M.Malfaz, “A New Approach to Modeling Emotions and Their Use on a Decision-Making System for Artificial Agents,” IEEE Trans. on Affctive Computing, Vol.3, No.1, pp. 56-68, 2012.
-  M. Haring, N. Bee, and E. Andre, “Creation and Evaluation of Emotion Expression with Body Movement, Sound and Eye Color for Humanoid Robots,” IEEE Int. Symposium on Robot and Human Interactive Communication, Atlanta, America, pp. 204-209, 2011.
-  M. A. Salichs, R. Barber, A. M. Khamis et al., “Maggie: A Robotic Platform for Human-Robot Social Interaction,” IEEE Conf. on Robotics, Autonomous and Mechatronics, Bangkok, Thailand, pp. 1-7, 2006.
-  M. Hashimoto, M. Yamano, and T. Usui, “Effects of Emotional Synchronization in Human-Robot KANSEI Communications,” IEEE Int. Symposium on Robot and Human Interactive Communication, Toyama, Japan, pp. 52-57, 2009.
-  Y. Yamazaki, Y. Hatakeyama, F.-Y. Dong, K. Nomoto, and K. Hirota, “Fuzzy Inference based Mentality Expression for Eye Robot in Affinity Pleasure-Arousal Space,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.3, pp. 304-313, 2008.
-  Z.-L. Liu,M.Wu, D.-Y. Li, Y. Yamazaki, F.-Y. Dong, and K. Hirota, “Emotional States Based 3-D Fuzzy atmosfield for Casual Communication between Humans and Robots,” IEEE Int. Conf. on Fuzzy Systems, Taipei, Taiwan, pp. 777-782, 2011.
-  Y. Yamazaki, F.-Y. Dong, Y. Masuda, Y. Uehara, P. Kormushev, H. A. Vu, P. Q. Le, and K. Hirota, “Intent Expression Using Eye Robot forMascot Robot System,” 8th Int. Symposium on Advanced Intelligent Systems, Sokcho, Korea, pp. 576-580, 2007.
-  S. Singh, T. Jaakkola, M. L. Littman, and C. Szepesvari, “Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms,” Machine Learning, Vol.38, No.3, pp. 287-308, 2000.
-  N. Karakayali, “Social Distance and Affective Orientations,” Sociological Forum, Vol.24, No.3, pp. 538-562, 2009.
-  Z.-T. Liu, M. Wu, D.-Y. Li, L-F. Chen, F.-Y. Dong, Y. Yamakaki, and K. Hirota, “Concept of Fuzzy Atmosfield for Representing Communication Atmosphere and Its Application to Humans-Robots Interaction,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.1, pp. 3-17, 2013.
-  N. Mitssunaga, T. Miyashita, H. Ishiguro, K. Kogure, and N. Hagita, “Robovie-IV: A Communication RobotInteracting with People Daily in an Office,” Proc. of Int. Conf. on Intelligent Robots and System, Beijing, China, pp. 5066-5072, 2006.
-  Z.-T. Liu, Z. Mu, L.-F. Chen et al., “Emotion Recognition of Violin Music based on Strings Music Theory for Mascot Robot System,” 9th Int. Conf. on Informatics in Control, Automation and Robotics, Rome, Italy, pp. 5-14, 2012.
-  E. S. Kim and B. Scassellati, “Learning to Refine Behavior Using Prosodic Feedback,” Proc. of the Int. Conf. on Development and Learning, London, England, pp. 205-210, 2007.
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