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A hierarchical network for learning robust models of kinematic chains

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

A hierarchical network for visuo-motor coordination is proposed. The hierarchical approach allows learning geometric models of realistic robots with six or more axes. The network consists of several one-dimensional subnetworks, which learn the coordinate transform and rotation axis for each joint. In our simulation, the network reduces the end-effector error of a 7-axis anthropomorphic robot and 20-axis robot below the visual error.

Supported by a grant from the DFG (GK KOGNET).

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References

  1. J. J. Craig. Introduction to Robotics (2nd ed.), Addison-Wesley Publishing Company, 1989

    Google Scholar 

  2. H. J. Ritter, T. M. Martinetz, K. J. Schulten. Topology-concerving maps for learning visuo-motor-coordination. Neural Networks, Vol. 2:, pp. 159–168, 1989

    Google Scholar 

  3. J. Paillard. Brain and Space, Oxford University Press, 1991

    Google Scholar 

  4. Y. Burnod, P. Grandguillaume, I. Otto, S. Ferraine, P. B. Johnson, R. Caminiti. Visuomotor transformations underlying arm movements toward visual targets: A Neural Network model of Cerebral Cortical Operations. The Journal of Neuroscience, Vol. 12(4), pp. 1435–1453, April 1992

    Google Scholar 

  5. B. Fritzke. A growing neural gas network learns topologies. In G. Tesauro,D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems, Vol. 7, pp 625–632. MIT Press, Cambrige MA, 1995

    Google Scholar 

  6. B. Fritzke. Incremental learning of local linear mappings. In F. Fogelman, editor, ICANN'95: International Conference on Artificial Neural Networks, pp. 217–222, Paris, France, 1995. EC2 & Cie.

    Google Scholar 

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Authors and Affiliations

  1. Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany

    Eric Maël

Authors
  1. Eric Maël
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Maël, E. (1996). A hierarchical network for learning robust models of kinematic chains. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_105

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  • DOI: https://doi.org/10.1007/3-540-61510-5_105

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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