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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/9753

Title: Goal-directed Imitation with Self-adjusting Adaptor Based on a Neural Oscillator Network
Authors: Yang, Woosung
Chong, Nak Young
Keywords: Humanoid Robot
Biped locomotion
Imitation learning
Self-adjusting adaptor
Neural oscillator
Issue Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 12th International Conference on Advanced Robotics, 2005. ICAR '05. Proceedings
Start page: 404
End page: 410
DOI: 10.1109/ICAR.2005.1507442
Abstract: An innovative framework of imitation between dissimilar bodies is proposed to automatically achieve the goal of the perceived behavior. Biologically inspired control based on central pattern generators currently gains increasing attention to embody human-like rhythmic motions to humanoid robots. However, this control approach suffers from highly nonlinear dynamics of neural systems, difficulty of motion pattern generation, uncertainty of behavior between neural systems and biomechanics, and so on. To cope with these problems, the imitation technique is employed in this work. We first propose the self-adjusting adaptor to easily generate an appropriate motion pattern by modifying the perceived motion toward attaining the goal of the behavior. Secondly, we verify the property of entrapment of neural oscillator network in the proposed adaptor to duplicate the regenerated motion pattern. In the numerical simulations of biped locomotion, the perceived pattern data is regenerated to keep the direction of the foot contact force identical between the demonstrator and the imitator Also, the neural oscillator is entrained by external signals under stable conditions. To the best of the authors' knowledge, this paper is the first work to validate the advantages of neural oscillator networks as a tool of imitation
Rights: Copyright (C) 2005 IEEE. Reprinted from 12th International Conference on Advanced Robotics, 2005. ICAR '05. Proceedings, 2005, 404-410. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of JAIST's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://hdl.handle.net/10119/9753
Material Type: publisher
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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