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.
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References
Sakata, H., Taira, M., Kusunoki, M., Murata, A., Tanaka, Y.: The Parietal Association Cortex in Depth Perception and Visual Control of Hand Action. Trend in Neuroscience 20(8), 350–357 (1997)
Shibata, K.: A Neural-Network to Get Correlated Information among Multiple Inputs. In: Proc. of IJCNN 1993 NAGOYA, vol. 3, pp. 2532–2535 (1993)
Cottrell, G.W., Munro, P., Zipser, D.: Image Compression by Back–Propagation: an Example of Extensional Programming. In: Sherky, N.E. (ed.) Advances in Cognitive Science, vol. 3. Ablex, Norwood (1988)
Uno, Y., Fukumura, N., Suzuki, R., Kawato, M.: A Computational Model For Recognizing Objects and Planning Hand Shapes in Grasping Movements. Neural Networks 8(6), 839–851 (1995)
Fukumura, N., Otane, S., Uno, Y., Suzuki, R.: A Neural Network Model for Extracting Correlated Information in Sensory Integration. In: Proceedings of ICONIP, vol. 2, pp. 873–876 (1998)
DeMers, D., Cottrell, G.: Non-Linear Dimensionality Reduction. Advances in Neural Information Processing Systems 5, 580–587 (1992)
Noda, I.: Acquisition of Internal Representation using Overload Learning. –Case Study of Learning Identity-function–. Japan IEICE Technical Report NC94-34, pp. 15–22 (1994) (in Japanese)
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Fukumura, N., Wakaki, K., Uno, Y. (2005). A Modular Structure of Auto-encoder for the Integration of Different Kinds of Information. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_37
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DOI: https://doi.org/10.1007/11539087_37
Publisher Name: Springer, Berlin, Heidelberg
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