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International Conference on Simulation of Adaptive Behavior

SAB 2014: From Animals to Animats 13 pp 188–197Cite as

Unsupervised Learning of Sensory Primitives from Optical Flow Fields

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8575))

Abstract

Adaptive behaviour of animats largely depends on the processing of their sensory information. In this paper, we examine the estimation of robot egomotion from visual input by unsupervised online learning. The input is a sparse optical flow field constructed from discrete motion detectors. The global flow field properties depend on the robot motion, the spatial distribution of motion detectors with respect to the robot body and the visual environment. We show how online linear Principal Component Analysis can be applied to this problem to enable a robot to continuously adapt to a changing environment.

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Author information

Authors and Affiliations

  1. Cognitive Robotics Group, Dept. of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489, Berlin, Germany

    Oswald Berthold & Verena V. Hafner

Authors
  1. Oswald Berthold
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  2. Verena V. Hafner
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Editor information

Editors and Affiliations

  1. Robotic Intelligence Lavoratory, Jaume I University, Avda. Sos Baynat s/n, 12071, Castellón de la Plana, Spain

    Angel P. del Pobil

  2. School of Computing, University of Leeds, LS2 9JT, Leeds, UK

    Eris Chinellato

  3. Robotic Intelligence Laboratory, Jaume I University, Avda. Sos Baynat s/n, 12071, Castellón de la Plana, Spain

    Ester Martinez-Martin  & Enric Cervera  & 

  4. Mærsk McKinney Møller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark

    John Hallam

  5. Robotic Intelligence Laboratory, Jaume I University, Avda. Sos Baynat s/n, 12071, Castellón de la Plana, spain

    Antonio Morales

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© 2014 Springer International Publishing Switzerland

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Berthold, O., Hafner, V.V. (2014). Unsupervised Learning of Sensory Primitives from Optical Flow Fields. In: del Pobil, A.P., Chinellato, E., Martinez-Martin, E., Hallam, J., Cervera, E., Morales, A. (eds) From Animals to Animats 13. SAB 2014. Lecture Notes in Computer Science(), vol 8575. Springer, Cham. https://doi.org/10.1007/978-3-319-08864-8_18

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  • DOI: https://doi.org/10.1007/978-3-319-08864-8_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08863-1

  • Online ISBN: 978-3-319-08864-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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