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JACIII Vol.19 No.4 pp. 532-543
doi: 10.20965/jaciii.2015.p0532
(2015)

Paper:

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Motion Segmentation and Recognition for Imitation Learning and Influence of Bias for Learning Walking Motion of Humanoid Robot Based on Human Demonstrated Motion

Yasutake Takahashi*, Hiroki Hatano*, Yosuke Maida**, Kazuyuki Usui**, and Yoichiro Maeda***

*Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

**Department of Human and Artificial Intelligent Systems, Faculty of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

***Department of Robotics, Faculty of Engineering, Osaka Institute of Technology
5-16-1 Omiya, Asahi-ku, Osaka 535-8585, Japan

Received:
April 24, 2014
Accepted:
May 27, 2015
Published:
July 20, 2015
Creative Commons License
Keywords:
motion segmentation and recognition, imitation learning, learning bias, humanoid robot, via-point representation
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.
Cite this article as:
Y. Takahashi, H. Hatano, Y. Maida, K. Usui, and Y. Maeda, “Motion Segmentation and Recognition for Imitation Learning and Influence of Bias for Learning Walking Motion of Humanoid Robot Based on Human Demonstrated Motion,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.4, pp. 532-543, 2015.
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Last updated on Apr. 19, 2024