Inter-modality mapping in robot with recurrent neural network

doi:10.1016/j.patrec.2010.05.002

2010-09-01

PRE

DOI：10.1016/j.patrec.2010.05.002

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摘要

En 中文

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. (C) 2010 Elsevier B.V. All rights reserved.

Keyword:

Dynamical systems

Inter-modal mapping

Recurrent neural network with parametric bias

Generalization