Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot

Authors:
Naoyuki Kubota;Yusuke Nojima;Fumio Kojima;Toshio Fukuda
Affiliations:
Department of System Design, Tokyo Metropolitan University, PREST, Japan Science and Technology Agency, 1-1 Minami-Ohsawa, 192-0397, Tokyo, Hachioji, Japan;Department of Computer Science and Intelligent Systems, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Osaka, Sakai, Japan;Department of Mechanical and Systems Engineering, Graduate School of Kobe University, 1-1 Rokkodai-cho, 657-8501, Kobe, Nada-ku, Japan;Department of Micro System Engineering, Graduate School of Nagoya University, 1-1 Rokkodai-cho, 464-8603, Nagoya, Chigusa-ku, Japan
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2006

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Abstract

The purpose of this study is to develop partner robots that can obtain and accumulate human-friendly behaviors. To achieve this purpose, the entire architecture of the robot is designed, based on a concept of structured learning which emphasizes the importance of interactive learning of several modules through interaction with its environment. This paper deals with a trajectory planning method for generating hand-to-hand behaviors of a partner robot by using multiple fuzzy state-value functions, a self-organizing map, and an interactive genetic algorithm. A trajectory for the behavior is generated by an interactive genetic algorithm using human evaluation. In order to reduce human load, human evaluation is estimated by using the fuzzy state-value function. Furthermore, to cope with various situations, a self-organizing map is used for clustering a given task dependent on a human hand position. And then, a fuzzy state-value function is assigned to each output unit of the self-organizing map. The robot can easily obtain and accumulate human-friendly trajectories using a fuzzy state-value function and a knowledge database corresponding to the unit selected in the self-organizing map. Finally, multiple fuzzy state-value functions can estimate a human evaluation model for the hand-to-hand behaviors. Several experimental results show the effectiveness of the proposed method.