A modular structure of auto-encoder for the integration of different kinds of information

Authors:
Naohiro Fukumura;Keitaro Wakaki;Yoji Uno
Affiliations:
Department of Information and Computer Sciences, Toyohashi University of Technology, Toyohashi, Aichi, Japan;Department of Information and Computer Sciences, Toyohashi University of Technology, Toyohashi, Aichi, Japan;Department of Information and Computer Sciences, Toyohashi University of Technology, Toyohashi, Aichi, Japan
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

Citing 2
Cited 0

A computational model for recognizing objects and planning hand shapes in grasping movements

Neural Networks
Non-Linear Dimensionality Reduction

Advances in Neural Information Processing Systems 5, [NIPS Conference]

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Abstract

Humans use many different kinds of information from different sensory organs in motion tasks. It is important in human sensing to extract useful information and effectively use the multiple kinds of information. From the viewpoint of a computational theory, we approach the integration mechanism of human sensory and motor information. In this study, the modular structure of auto-encoder is introduced to extract the intrinsic properties about a recognized object that are contained commonly in multiple kind of information. After the learning, the relaxation method using the learned model can solve the transformation between the integrated kinds of information. This model was applied to the problem how a locomotive robot decides a leg's height to climb over an obstacle from the visual information.