標題: Robot learning schemes that trade motion accuracy for command simplification
作者: Young, KY
Lee, JF
Jou, HJ
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: robotics;learning control;command simplification;human motor control;fuzzy neural network
公開日期: 16-Mar-2000
摘要: This study was inspired by the human motor control system in its ability to accommodate a wide variety of motions. By contrast, the biologically inspired robot learning controller usually encounters huge learning space problems in many practical applications. A hypothesis for the superiority of the human motor control system is that it may have simplified the motion command at the expense of motion accuracy. This tradeoff provides an insight into how fast and simple control can be achieved when a robot task does not demand high accuracy. Two motion command simplification schemes are proposed in this paper based on the equilibrium-point hypothesis for human motion control. Investigation into the tradeoff between motion accuracy and command simplification reported in this paper was conducted using robot manipulators to generate signatures. Signature generation involves fast handwriting, and handwriting is a human skill acquired via practice. Because humans learn how to sign their names after they learn how to write, in the second learning process, they somehow learn to trade motion accuracy for motion speed and command simplicity, since signatures are simplified forms of original handwriting. Experiments are reported that demonstrate the effectiveness of the proposed schemes. (C) 2000 Elsevier Science B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/S0165-0114(98)00062-1
http://hdl.handle.net/11536/30651
ISSN: 0165-0114
DOI: 10.1016/S0165-0114(98)00062-1
期刊: FUZZY SETS AND SYSTEMS
Volume: 110
Issue: 3
起始頁: 313
結束頁: 329
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