Modular Fuzzy Neural Networks for Imitative Learning of A Partner Robot

Imitation is a powerful tool for behavior learning and human communication. Basically, imitative learning is composed of model observation and model reproduction. This paper applies a spiking neural network and self-organizing map for model observation, and modular fuzzy neural networks and a steady-state genetic algorithm for model reproduction. The proposed method is applied for a partner robot interacting with a human. Experimental results show that the proposed method enables a robot to learn behaviors through imitation and can interact with a human efficiently.

[1]  Fumio Kojima,et al.  Fuzzy and Neural Computing for Communication of a Partner Robot , 2003, J. Multiple Valued Log. Soft Comput..

[2]  M. Arbib,et al.  Language within our grasp , 1998, Trends in Neurosciences.

[3]  Naoyuki Kubota,et al.  Fuzzy Computing for Communication of A Partner Robot Based on Imitation , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[4]  Mitsuo Kawato,et al.  MOSAIC Model for Sensorimotor Learning and Control , 2001, Neural Computation.

[5]  Yoshihiko Nakamura,et al.  Embodied Symbol Emergence Based on Mimesis Theory , 2004, Int. J. Robotics Res..

[6]  Christopher J. Bishop,et al.  Pulsed Neural Networks , 1998 .

[7]  Mitsuo Kawato,et al.  Multiple Paired Forward-Inverse Models for Human Motor Learning and Control , 1998, NIPS.

[8]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[9]  Toshio Fukuda,et al.  An intelligent robotic system based on a fuzzy approach , 1999, Proc. IEEE.

[10]  Jeffrey L. Krichmar,et al.  Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines , 2001, Complex..

[11]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[12]  Naoyuki Kubota,et al.  Action learning of a mobile robot based on perceiving-acting cycle , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[13]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[14]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[15]  Naoyuki Kubota,et al.  Behavior Coordination of A Partner Robot based on Imitation , 2004 .

[16]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[17]  G. Rizzolatti,et al.  Premotor cortex and the recognition of motor actions. , 1996, Brain research. Cognitive brain research.

[18]  Richard S. Sutton,et al.  Reinforcement Learning , 1992, Handbook of Machine Learning.

[19]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[20]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[21]  Naoyuki Kubota,et al.  Local episode-based learning of multi-objective behavior coordination for a mobile robot in dynamic environments , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..