Advertisement

Direct Control of an Active Tactile Sensor Using Echo State Networks

  • André Frank Krause
  • Bettina Bläsing
  • Volker Dürr
  • Thomas Schack
Part of the Cognitive Systems Monographs book series (COSMOS, volume 6)

Abstract

Tactile sensors (antennae) play an important role in the animal kingdom. They are also very useful as sensors in robotic scenarios, where vision systems may fail. Active tactile movements increase the sampling performance. Here we directly control movements of the antenna of a simulated hexapod using an echo state network (ESN). ESNs can store multiple motor patterns as attractors in a single network and generate novel patterns by combining and blending already learned patterns using bifurcation inputs.

Keywords

Humanoid Robot Tactile Sensor Single Network Echo State Network Open Dynamics Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dürr, V., Krause, A.: The stick insect antenna as a biological paragon for an actively moved tactile probe for obstacle detection. In: Berns, K., Dillmann, R. (eds.) Climbing and walking robots - from biology to industrial applications, Proceeding of Fourth International Conference Climbing and Walking Robots (CLAWAR 2001), pp. 87–96. Professional Engineering Publishing, Bury St. Edmunds (2001)Google Scholar
  2. 2.
    Dürr, V., Krause, A.F., Neitzel, M., Lange, O., Reimann, B.: Bionic tactile sensor for near-range search, localisation and material classification. In: Berns, K., Luksch, T. (eds.) Autonome Mobile Systeme 2007. Fachgespräch Kaiserslautern, vol. 20, pp. 240–246. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Haruno, M., Wolpert, D.M., Kawato, M.: Mosaic model for sensorimotor learning and control. Neural Computation 13(10), 2201–2220 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Hochreiter, S., Bengio, Y.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: Kremer, S.C., Kolen, J.F. (eds.) A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press, Los Alamitos (2001)Google Scholar
  5. 5.
    Hogan, N.: An organizing principle for a class of voluntary movements. Journal of Neuroscience 4, 2745–2754 (1984)Google Scholar
  6. 6.
    Jaeger, H.: Adaptive nonlinear system identification with echo state networks. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15, pp. 593–600. MIT Press, Cambridge (2002)Google Scholar
  7. 7.
    Jaeger, H.: Tutorial on training recurrent neural networks, covering bppt, rtrl, ekf and the ”‘echo state network”‘ approach. Tech. Rep. GMD Report 159, German National Research Center for Information Technology (2002)Google Scholar
  8. 8.
    Jaeger, H., Lukosevicius, M., Popovici, D., Siewert, U.: Optimization and applications of echo state networks with leaky integrator neurons. Neural Networks 20(3), 335–352 (2007)zbMATHCrossRefGoogle Scholar
  9. 9.
    Jäger, H.: Generating exponentially many periodic attractors with linearly growing echo state networks. Technical report 3, IUB (2006)Google Scholar
  10. 10.
    Jäger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004)CrossRefGoogle Scholar
  11. 11.
    Kaneko, M., Kanayma, N., Tsuji, T.: Active antenna for contact sensing. IEEE Transactions on Robotics and Automation 14, 278–291 (1998)CrossRefGoogle Scholar
  12. 12.
    Kramer, O.: Fast blackbox optimization: Iterated local search and the strategy of powell. In: The 2009 International Conference on Genetic and Evolutionary Methods, GEM 2009 (in press, 2009)Google Scholar
  13. 13.
    Krause, A.F., Bläsing, B., Schack, T.: Modellierung kognitiver Strukturen mit hierarchischen selbstorganisierenden Karten. In: Pfeffer, I., Alfermann, D. (eds.) 41. Jahrestagung der Arbeitsgemeinschaft für Sportpsychologie (asp), vol. 188, p. 91. Czwalina Verlag Hamburg (2009)Google Scholar
  14. 14.
    Krause, A.F., Dürr, V.: Tactile efficiency of insect antennae with two hinge joints. Biological Cybernetics 91, 168–181 (2004)zbMATHCrossRefGoogle Scholar
  15. 15.
    Krause, A.F., Schütz, C., Dürr, V.: Active tactile sampling patterns during insect walking and climbing. In: Proc. Göttingen Neurobiol. Conf., vol. 31 (2007)Google Scholar
  16. 16.
    Lange, O., Reimann, B.: Vorrichtung und Verfahren zur Erfassung von Hindernissen. German Patent 102005005230 (2005)Google Scholar
  17. 17.
    Reinhart, R.F., Steil, J.J.: Attractor-based computation with reservoirs for online learning of inverse kinematics. In: European Symposium on Artificial Neural Networks (ESANN) – Advances in Computational Intelligence and Learning (2009)Google Scholar
  18. 18.
    Rolf, M., Steil, J.J., Gienger, M.: Efficient exploration and learning of whole body kinematics. In: IEEE 8th International Conference on Development and Learning (2009)Google Scholar
  19. 19.
    Schack, T., Mechsner, F.: Representation of motor skills in human long-term memory. Neuroscience Letters 391, 77–81 (2006)CrossRefGoogle Scholar
  20. 20.
    Staudacher, E., Gebhardt, M.J., Dürr, V.: Antennal movements and mechanoreception: neurobiology of active tactile sensors. Adv. Insect. Physiol. 32, 49–205 (2005)CrossRefGoogle Scholar
  21. 21.
    Steil, J.J.: Backpropagation - decorrelation: online recurrent learning with o(n) complexity. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 843–848 (2004)Google Scholar
  22. 22.
    Steil, J.J.: Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks 20(3), 353–364 (2007)zbMATHCrossRefGoogle Scholar
  23. 23.
    Tani, J., Nolfi, S.: Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Neural Networks 12, 1131–1141 (1999)CrossRefGoogle Scholar
  24. 24.
    Tani, J.: On the interactions between top-down anticipation and bottom-up regression. Frontiers in Neurorobotics 1, 2 (2007)CrossRefGoogle Scholar
  25. 25.
    Tani, J., Itob, M., Sugitaa, Y.: Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB. Neural Networks 17, 1273–1289 (2004)CrossRefGoogle Scholar
  26. 26.
    Ueno, N., Svinin, M., Kaneko, M.: Dynamic contact sensing by flexible beam. IEEE/ASME Transactions on Mechatronics 3, 254–264 (1998)CrossRefGoogle Scholar
  27. 27.
    Vrugt, J., Robinson, B., Hyman, J.: Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Transactions on Evolutionary Computation 13(2), 243–259 (2008)CrossRefGoogle Scholar
  28. 28.
    Webb, B.: Neural mechanisms for prediction: do insects have forward models? Trends in Neuroscience 27, 278–282 (2004)CrossRefGoogle Scholar
  29. 29.
    Webots: Commercial Mobile Robot Simulation Software, http://www.cyberbotics.com
  30. 30.
    Werbos, P.: Backpropagation through time: what it does and how to do it. Proceedings of the IEEE 78(10), 1550–1560 (1990)CrossRefGoogle Scholar
  31. 31.
    Wiener, N.: Extrapolation, interpolation, and smoothing of stationary time series with engineering applications. Cambridge, Technology Press of Massachusetts Institute of Technology and New York, Wiley (1949) Google Scholar
  32. 32.
    Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1(2), 270–280 (1989), http://www.mitpressjournals.org/doi/abs/10.1162/neco.1989.1.2%.270 CrossRefGoogle Scholar
  33. 33.
    Yamashita, Y., Tani, J.: Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Computational Biology 4(11) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • André Frank Krause
    • 1
    • 3
  • Bettina Bläsing
    • 1
    • 3
  • Volker Dürr
    • 2
    • 3
  • Thomas Schack
    • 1
    • 3
  1. 1.Neurocognition and Action - Biomechanics Research GroupBielefeld UniversityGermany
  2. 2.Dept. for Biological CyberneticsUniversity of Bielefeld, Faculty of BiologyBielefeldGermany
  3. 3.Cognitive Interaction Technology, Center of ExcellenceUniversity of BielefeldBielefeldGermany

Personalised recommendations