Texture Discrimination with Artificial Whiskers in the Robot-Rat Psikharpax

  • Steve N’Guyen
  • Patrick Pirim
  • Jean-Arcady Meyer
Part of the Communications in Computer and Information Science book series (CCIS, volume 127)


We describe a novel algorithm for texture discrimination which we tested on a robot using an artificial whisker system. Experiments on both fixed head and mobile platform have shown that this system is efficient and robust, with greater behavioral capacities than previous similar approaches, thus, demonstrating capabilities to complement or supply vision. Moreover, results tends to show that the length and number of whiskers may be an important parameter for texture discrimination. From a more fundamental point of view these results suggest that two currently opposing hypotheses to explain texture recognition in rats, namely the “kinetic signature hypothesis” and the “resonance hypothesis”, may be, in fact, complementary.


Mobile Robot Multi Layer Perceptron Haptic System Texture Discrimination Equivalent Noise Level 
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.


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  1. 1.
    Carvell, G., Simons, D.: Biometric analyses of vibrissal tactile discimination in the rat. Journal of Neuroscience 10, 2638–2648 (1990)Google Scholar
  2. 2.
    Guic-Robles, E., Valdivieso, C., Guarjardo, G.: Rats can learn a roughness discrimination using only their vibrissal system. Behavioural Brain Research 31, 285–289 (1989)CrossRefGoogle Scholar
  3. 3.
    Brecht, M., Preilowski, B., Merzenich, M.: Functional architecture of the mystacial vibrissae. Behavioural Brain Research 84, 81–97 (1997)CrossRefGoogle Scholar
  4. 4.
    Krupa, D.J., Matell, M.S., Brisben, A.J., Oliviera, L.M., Nicolelis, M.A.L.: Behavioural properties of the trigeminal somatosensory system in rats performing whisker-dependent tactile discrimination. J. Neurosci., 5752–5763 (2001)Google Scholar
  5. 5.
    Petersen, R.S., Diamond, M.E.: Spatial-Temporal Distribution of Whisker-Evoked Activity in Rat Somatosensory Cortex and the Coding of Stimulus Location. J. Neurosci. 20, 6135–6143 (2000)Google Scholar
  6. 6.
    Hartmann, M.J.: Active sensing capabilities of the rat whisker system. Autonomous Robots 11, 249–254 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Brooks, R.A.: A robot that walks: Emergent behaviors from a carefully evolved network. Technical Report AI MEMO 1091, MIT (1989)Google Scholar
  8. 8.
    Russell, R.A.: Object recognition using articulated whisker probes. In: Proc. 15th Int. Symp. Industr. Robots, pp. 605–612 (1985)Google Scholar
  9. 9.
    Chapman, T., Hayes, A., Tilden, T.: Reactive maze solving with a biologically-inspired wind sensor. In: Meyer, J., Berthoz, A., Floreano, D., Roitblat, H., Wilson, S. (eds.) From Animals to Animats 6. Proc. of the 6th Int. Conf. on Simulation of Adaptive Behavior, pp. 81–87. MIT PRESS, A Bradford Book (2000)Google Scholar
  10. 10.
    Fend, M., Bovet, S., Yokoi, H., Pfeifer, R.: An active artificial whisker array for texture discrimination. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. II, pp. 1044–1049 (2003)Google Scholar
  11. 11.
    Lungarella, M., Hafner, V., Pfeifer, R., Yokoi, H.: Artificial whisker sensors in robotics. In: IEEE/RSJ International Conference on Intelligent Robots and System, vol. 3, pp. 2931–2936 (2002)Google Scholar
  12. 12.
    Kim, D., Moller, R.: A biomimetic whisker for texture discrimination and distance estimation. From Animals to Animats 8, 140–149 (2004)Google Scholar
  13. 13.
    Seth, A.K., McKinstry, J.L., Edelman, G.M., Krichmar, J.L.: Spatiotemporal processing of whisker input supports texture discrimination by a brain-based device. In: Schall, S., Ijspeert, A., Billard, A., Vijayakumar, S., Hallam, J., Meyer, J. (eds.) From Animals to Animats 8. Proc. of the 8th Int. Conf. on Simulation of Adaptive Behavior. MIT Press, MA (2004)Google Scholar
  14. 14.
    Fox, C.W., Mitchinson, B., Pearson, M.J., Pipe, A.G., Prescott, T.J.: Contact type dependency of texture classification in a whiskered mobile robot. Autonomous Robots (2009) (in press)Google Scholar
  15. 15.
    Meyer, J.A., Guillot, A., Girard, B., Khamassi, M., Pirim, P., Berthoz, A.: The psikharpax project: Towards building an artificial rat. Robotics and Autonomous Systems 50, 211–223 (2005)CrossRefGoogle Scholar
  16. 16.
    N’Guyen, S., Pirim, P., Meyer, J.A.: Elastomer-based tactile sensor array for the artificial rat psikharpax. In: ISEF 2009 - XIV International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering (2009) (in press)Google Scholar
  17. 17.
    Arabzadeh, E., Panzeri, S., Diamond, M.E.: Whisker Vibration Information Carried by Rat Barrel Cortex Neurons. J. Neurosci. 24, 6011–6020 (2004)CrossRefGoogle Scholar
  18. 18.
    Arabzadeh, E., Zorzin, E., Diamond, M.E.: Neuronal encoding of texture in the whisker sensory pathway. PLoS Biol. 3, e17 (2005)CrossRefGoogle Scholar
  19. 19.
    Ghitza, O.: Auditory models and human performance in tasks related to speech coding and speech recognition. IEEE Transactions on Speech and Audio Processing 2, 115–132 (1994)CrossRefGoogle Scholar
  20. 20.
    Kim, D.S., Lee, S.Y., Kil, R.M.: Auditory processing of speech signals for robust speech recognition in real-world noisy environments. IEEE Transactions on Speech and Audio Processing 7, 55–69 (1999)CrossRefGoogle Scholar
  21. 21.
    Sreenivas, T.V., Niederjohn, R.J.: Spectral analysis for formant frequency estimation in noise. IEEE Transactions on Signal Processing 40, 282–293 (1992)CrossRefGoogle Scholar
  22. 22.
    Licklider, J.C.R., Pollack, I.: Effect of differentiation, integration, and infinite peak clipping upon the intelligibility of speech. Journal of the Acoustical Society of America 20, 42–52 (1948)CrossRefGoogle Scholar
  23. 23.
    Hipp, J., Arabzadeh, E., Zorzin, E., Conradt, J., Kayser, C., Diamond, M.E., Konig, P.: Texture Signals in Whisker Vibrations. J. Neurophysiol. 95, 1792–1799 (2006)CrossRefGoogle Scholar
  24. 24.
    Nissen, S.: Implementation of a Fast Artificial Neural Network Library (fann). Report, Department of Computer Science University of Copenhagen (DIKU) 31 (2003)Google Scholar
  25. 25.
    Igel, C., Hüskel, M.: Improving the rprop learning algorithm. In: Proceedings of the Second International Symposium on Neural Computation, NC 2000, pp. 115–121 (2000)Google Scholar
  26. 26.
    Fend, M.: Whisker-based texture discrimination on a mobile robot. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 302–311. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  27. 27.
    Moore, C.I., Andermann, M.L.: 2. In: The Vibrissa Resonance Hypothesis, pp. 21–60. CRC Press, Boca Raton (2005)Google Scholar
  28. 28.
    Neimark, M.A., Andermann, M.L., Hopfield, J.J., Moore, C.I.: Vibrissa resonance as a transduction mechanism for tactile encoding. The Journal of Neuroscience (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Steve N’Guyen
    • 1
    • 2
  • Patrick Pirim
    • 2
  • Jean-Arcady Meyer
    • 1
  1. 1.Institut des Systèmes Intelligents et de RobotiqueUniversité Pierre et Marie Curie-Paris 6, CNRS UMR 7222Paris Cedex 05France
  2. 2.Brain Vision SystemsParisFrance

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