Author:
Takahashi Yasutake, ,Hatano Hiroki,Maida Yosuke,Usui Kazuyuki,Maeda Yoichiro, ,
Abstract
Two main issues arise in practical imitation learning by humanoid robots observing human behavior – the first is segmenting and recognizing motion demonstrated naturally by a human beings and the second is utilizing the demonstrated motion for imitation learning. Specifically, the first involves motion segmentation and recognition based on the humanoid robot motion repertoire for imitation learning and the second introduces learning bias based on demonstrated motion in the humanoid robot’s imitation learning to walk. We show the validity of our motion segmentation and recognition in a practical way and report the results of our investigation in the influence of learning bias in humanoid robot simulations.
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
Reference14 articles.
1. H. Miyamoto and M. Kawato, “A tennis serve and upswing learning robot based on bi-directional theory,” Neural Networks, Vol.11, No.78, pp. 1331-1344, 1998.
2. T. Inamura, Y. Nakamura, and I. Toshima, “Embodied symbol emergence based on mimesis theory,” Int. J. of Robotics Research, Vol.23, No.4, pp. 363-377, 2004.
3. M. K. Y. Okuzawa, S. Kato and H. Itoh, “Acquisition and modification of motion knowledge using continuous hmms for motion imitation of humanoids,” IEEE Int. Symp. on Micro-Nano Mechatronics and Human Science, pp.586-591, 2009.
4. Y. Okuzawa, S. Kato, M. Kanoh, and H. Itoh, “Motion recognition and modifying motion generation for imitation robot based on motion knowledge formation,” The Trans. of the Institute of Electrical Engineers of Japan: C, A Publication of Electronics, Information and System Society, Vol.131, pp. 655-663, March 2011 (in Japanese).
5. Y. Okuzawa, S. Kato, M. Kanoh, and H. Itoh, “Motion recognition and modifying motion generation for imitation robot based on motion knowledge formation,” IEEJ Trans. on Electronics, Information and Systems, Vol.131, No.3, pp. 655-663, 2011.
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