Presentation 2006-10-19
Gesture and Sound Generation of Robot based on Cross-Modal Translation using Recurrent Neural Network with Parametric Bias
Tetsuya OGATA, Hideki KOZIMA, Kazunori KOMATANI, Hiroshi G. OKUNO,
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Abstract(in English) This paper proposes cross-modal mapping for a robot system to generate motions expressing auditory signals caused by the movements of objects and vice-versa. Since all correspondences between auditory signals and visual signals in the world are hard to memorize, the ability to generalize is indispensable. We adopted a neural circuit model called RNNPB, which has good generalization ability, for the learning model. We implemented the proposed system on the robot "Keepon." We taught it four kinds of events with the sounds of collision by manipulating a box object. Keepon behaved not only from learned events but also from unknown events. It could also generate various sounds according to observed motions.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Modality Translation / Keepon / Recurrent Neural Network with Parametric Bias
Paper # TL2006-22,NLC2006-18,PRMU2006-99
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Committee TL
Conference Date 2006/10/12(1days)
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Registration To Thought and Language (TL)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Gesture and Sound Generation of Robot based on Cross-Modal Translation using Recurrent Neural Network with Parametric Bias
Sub Title (in English)
Keyword(1) Modality Translation
Keyword(2) Keepon
Keyword(3) Recurrent Neural Network with Parametric Bias
1st Author's Name Tetsuya OGATA
1st Author's Affiliation Graduate School of Informatics, Kyoto University()
2nd Author's Name Hideki KOZIMA
2nd Author's Affiliation NICT
3rd Author's Name Kazunori KOMATANI
3rd Author's Affiliation Graduate School of Informatics, Kyoto University
4th Author's Name Hiroshi G. OKUNO
4th Author's Affiliation Graduate School of Informatics, Kyoto University
Date 2006-10-19
Paper # TL2006-22,NLC2006-18,PRMU2006-99
Volume (vol) vol.106
Number (no) 296
Page pp.pp.-
#Pages 6
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