USING SLOW FEATURE ANALYSIS TO IMPROVE THE REACTIVITY OF A HUMANOID ROBOT'S SENSORIMOTOR GAIT PATTERN

Sebastian Höfer, Manfred Hild

2010

Abstract

This paper presents an approach for increasing the reactivity of a humanoid robot’s gait, incorporating Slow Feature Analysis (SFA), an unsupervised learning algorithm issuing from the domain of theoretical biology. The main objective of this work is to find a means to detect disturbances in the gait pattern at an early stage without losing stability. Another goal is to investigate the general potential of SFA for using it within sensorimotor loops which to our knowledge has not been considered until now. The application of SFA within sensorimotor loops is motivated by pointing out its relation to second-order Volterra filters. Our experiments show that the overall reactivity of the gait pattern increases without any profound loss in stability, and that SFA appears to be suitable for the usage even at such levels of sensorimotor control that are directly involved into motor activity regulation.

References

  1. Aizerman, A., Braverman, E. M., and Rozoner, L. I. (1964). Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning. Automation and Remote Control, 25:821-837.
  2. Berkes, P. (2006). Temporal slowness as an unsupervised learning principle. PhD thesis, Humboldt-Universität zu Berlin.
  3. Berkes, P. and Wiskott, L. (2002). Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties. In Dorronsoro, J. R., editor, Proc. Intl. Conf. on Artificial Neural Networks - ICANN'02, Lecture Notes in Computer Science, pages 81-86. Springer.
  4. Berkes, P. and Wiskott, L. (2006). On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields. Neural Computation, 18(8):1868-1895.
  5. Franzius, M., Sprekeler, H., and Wiskott, L. (2007). Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Computational Biology, 3(8):e166.
  6. Lau, S., Leung, S., and Chan, B. (1992). A reduced rank second-order adaptive volterra filter. In ISSPA 92, Signal Processing and its Applications, pages 561-563, Gold Coast, Australia.
  7. Mathews, J. (1991). Adaptive polynomial filters. IEEE Signal Processing Magazine, 8(3):10-26.
  8. Pfeifer, R. and Bongard, J. C. (2006). How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books). The MIT Press.
  9. Spranger, M., Hö fer, S., and Hild, M. (2009). Biologically inspired posture recognition and posture change detection for humanoid robots. In Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 562-567, Guilin, China.
  10. Wiskott, L. (1998). Learning Invariance Manifolds. In Proc. of the 5th Joint Symp. on Neural Computation, May 16, San Diego, CA, volume 8, pages 196-203, San Diego, CA. Univ. of California.
  11. Wiskott, L. (2003). Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis.
  12. Wiskott, L. and Sejnowski, T. (2002). Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation, 14(4):715-770.
  13. Wyss, R., Kö nig, P., and Verschure, P. F. M. J. (2006). A model of the ventral visual system based on temporal stability and local memory. PLoS Biol, 4(5).
  14. Zaknich, A. (2005). Principles of adaptive filters and selflearning systems. Springer London.
  15. Zito, T., Wilbert, N., Wiskott, L., and Berkes, P. (2009). Modular toolkit for Data Processing (MDP): a Python data processing frame work.
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Paper Citation


in Harvard Style

Höfer S. and Hild M. (2010). USING SLOW FEATURE ANALYSIS TO IMPROVE THE REACTIVITY OF A HUMANOID ROBOT'S SENSORIMOTOR GAIT PATTERN . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 212-219. DOI: 10.5220/0003082102120219


in Bibtex Style

@conference{icnc10,
author={Sebastian Höfer and Manfred Hild},
title={USING SLOW FEATURE ANALYSIS TO IMPROVE THE REACTIVITY OF A HUMANOID ROBOT'S SENSORIMOTOR GAIT PATTERN},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={212-219},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003082102120219},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - USING SLOW FEATURE ANALYSIS TO IMPROVE THE REACTIVITY OF A HUMANOID ROBOT'S SENSORIMOTOR GAIT PATTERN
SN - 978-989-8425-32-4
AU - Höfer S.
AU - Hild M.
PY - 2010
SP - 212
EP - 219
DO - 10.5220/0003082102120219