Abstract
A system for mapping between different sensory modalities was developed for a robot system to enable it to generate motions expressing auditory signals and sounds generated by object movement. A recurrent neural network model with parametric bias, which has good generalization ability, is used as a learning model. Since the correspondences between auditory signals and visual signals are too numerous to memorize, the ability to generalize is indispensable. This system was implemented in the "Keepon" robot, and the robot was shown horizontal reciprocating or rotating motions with the sound of friction and falling or overturning motion with the sound of collision by manipulating a box object. Keepon behaved appropriately not only from learned events but also from unknown events and generated various sounds in accordance with observed motions.
| Original language | English |
|---|---|
| Pages (from-to) | 1560-1569 |
| Number of pages | 10 |
| Journal | Pattern Recognition Letters |
| Volume | 31 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2010 Sep 1 |
| Externally published | Yes |
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Keywords
- Dynamical systems
- Generalization
- Inter-modal mapping
- Recurrent neural network with parametric bias
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Inter-modality mapping in robot with recurrent neural network. / Ogata, Tetsuya; Nishide, Shun; Kozima, Hideki; Komatani, Kazunori; Okuno, Hiroshi G.
In: Pattern Recognition Letters, Vol. 31, No. 12, 01.09.2010, p. 1560-1569.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Inter-modality mapping in robot with recurrent neural network
AU - Ogata, Tetsuya
AU - Nishide, Shun
AU - Kozima, Hideki
AU - Komatani, Kazunori
AU - Okuno, Hiroshi G.
PY - 2010/9/1
Y1 - 2010/9/1
N2 - A system for mapping between different sensory modalities was developed for a robot system to enable it to generate motions expressing auditory signals and sounds generated by object movement. A recurrent neural network model with parametric bias, which has good generalization ability, is used as a learning model. Since the correspondences between auditory signals and visual signals are too numerous to memorize, the ability to generalize is indispensable. This system was implemented in the "Keepon" robot, and the robot was shown horizontal reciprocating or rotating motions with the sound of friction and falling or overturning motion with the sound of collision by manipulating a box object. Keepon behaved appropriately not only from learned events but also from unknown events and generated various sounds in accordance with observed motions.
AB - A system for mapping between different sensory modalities was developed for a robot system to enable it to generate motions expressing auditory signals and sounds generated by object movement. A recurrent neural network model with parametric bias, which has good generalization ability, is used as a learning model. Since the correspondences between auditory signals and visual signals are too numerous to memorize, the ability to generalize is indispensable. This system was implemented in the "Keepon" robot, and the robot was shown horizontal reciprocating or rotating motions with the sound of friction and falling or overturning motion with the sound of collision by manipulating a box object. Keepon behaved appropriately not only from learned events but also from unknown events and generated various sounds in accordance with observed motions.
KW - Dynamical systems
KW - Generalization
KW - Inter-modal mapping
KW - Recurrent neural network with parametric bias
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U2 - 10.1016/j.patrec.2010.05.002
DO - 10.1016/j.patrec.2010.05.002
M3 - Article
VL - 31
SP - 1560
EP - 1569
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
SN - 0167-8655
IS - 12
ER -