1P1-E07 Learning Android Control using Growing Neural Networks
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概要
- 論文の詳細を見る
Fixed sized neural networks are a popular technique for learning the adaptive control of non-linear plants. When applied to the complex control of android robots, however, they suffer from serious limitations, such as the moving target problem i.e. the interference between old and newly learned knowledge. To overcome these problems, we propose the use of growing neural networks in a new learning framework based on the process of consolidation. The new framework is able to overcome the drawbacks of sigmoidal neural networks, while maintaining their power of generalization. In experiments the framework was successfully applied to the control of an android robot.
著者
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Minato Takashi
Department Of Adaptive Machine Systems Graduate School Of Engineering Osaka University
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Ishiguro Hiroshi
Department Of Adaptive Machine Systems Graduate School Of Engineering Osaka University
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Minato Takashi
Department Of Adaptive Machine Systems Osaka University
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Amor Heni
Department of Adaptive Machine Systems Osaka University
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Ikemoto Shuhei
Department of Adaptive Machine Systems Osaka University
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Ishiguro Hiroshi
Osaka University:jst Erato
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