TACTILE TEXTURE DISCRIMINATION IN THE ROBOT-RAT PSIKHARPAX

Steve N'Guyen, Patrick Pirim, Jean-Arcady Meyer

2010

Abstract

We endowed a whiskered robot with a simple algorithm allowing to discriminate textures. Its efficiency and robustness have been demonstrated using both a fixed head and a mobile platform. Comparatively to previous similar approaches, this system affords greater behavioral capacities and proves to be able to complement or supply vision in simple navigation tasks. The corresponding results suggest that the length and number of the whiskers involved play a role in texture discrimination. They also suggest that two hypotheses that are currently considered as mutually exclusive to explain texture recognition in rats - i.e., the “kinetic signature hypothesis” and the “resonance hypothesis” - may be, in fact, complementary.

References

  1. Arabzadeh, E., Panzeri, S., and Diamond, M. E. (2004). Whisker Vibration Information Carried by Rat Barrel Cortex Neurons. J. Neurosci., 24(26):6011-6020.
  2. Arabzadeh, E., Zorzin, E., and Diamond, M. E. (2005). Neuronal encoding of texture in the whisker sensory pathway. PLoS Biol, 3(1):e17.
  3. Brecht, M., Preilowski, B., and Merzenich, M. (1997). Functional architecture of the mystacial vibrissae. Behavioural Brain Research, 84(1-2):81-97.
  4. Brooks, R. A. (1989). A robot that walks: Emergent behaviors from a carefully evolved network. Technical Report AI MEMO 1091, MIT.
  5. Carvell, G. and Simons, D. (1990). Biometric analyses of vibrissal tactile discimination in the rat. Journal of Neuroscience, 10(8):2638-2648.
  6. Chapman, T., Hayes, A., and Tilden, T. (2000). Reactive maze solving with a biologically-inspired wind sensor. In J. Meyer, A. Berthoz, D. F., Roitblat, H., and Wilson, S., editors, From Animals to Animats 6. Proc. of the 6th Int. Conf. on Simulation of Adaptive Behavior, pages 81-87. MA: MIT PRESS. A Bradford Book.
  7. Fend, M. (2005). Whisker-based texture discrimination on a mobile robot. Advances in Artificial Life - Proceedings of the 8th European Conference on Artificial Life (ECAL), pages 302-312.
  8. Fend, M., Bovet, S., Yokoi, H., and Pfeifer, R. (2003). An active artificial whisker array for texture discrimination. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), volume II, pages 1044-1049.
  9. Fox, C. W., Mitchinson, B., Pearson, M. J., Pipe, A. G., and Prescott, T. J. (2009). Contact type dependency of texture classification in a whiskered mobile robot. Autonomous Robots. In press.
  10. Ghitza, O. (1994). Auditory models and human performance in tasks related to speech coding and speech recognition. IEEE Transactions on Speech and Audio Processing, 2(1):115-132.
  11. Guic-Robles, E., Valdivieso, C., and Guarjardo, G. (1989). Rats can learn a roughness discrimination using only their vibrissal system. Behavioural Brain Research, 31(3):285-289.
  12. Hartmann, M. J. (2001). Active sensing capabilities of the rat whisker system. Autonomous Robots, 11:249-254.
  13. Hipp, J., Arabzadeh, E., Zorzin, E., Conradt, J., Kayser, C., Diamond, M. E., and Konig, P. (2006). Texture Signals in Whisker Vibrations. J Neurophysiol, 95(3):1792-1799.
  14. Igel, C. and Hüskel, M. (2000). Improving the rprop learning algorithm. In Proceedings of the Second International Symposium on Neural Computation, NC2000, pages 115-121.
  15. Kim, D.-S., Lee, S.-Y., and Kil, R. M. (1999). Auditory processing of speech signals for robust speech recognition in real-world noisy environments. IEEE Transactions on Speech and Audio Processing, 7(1):55-69.
  16. Kim, D. and Moller, R. (2004). A biomimetic whisker for texture discrimination and distance estimation. From Animals to Animats 8, pages 140-149.
  17. Krupa, D. J., Matell, M. S., Brisben, A. J., Oliviera, L. M., and Nicolelis, M. A. L. (2001). Behavioural properties of the trigeminal somatosensory system in rats performing whisker-dependent tactile discrimination. J. Neurosci., (21):5752-5763.
  18. Licklider, J. C. R. and Pollack, I. (1948). Effect of differentiation, integration, and infinite peak clipping upon the intelligibility of speech. Journal of the acoustical society of america, 20(1):42-52.
  19. Lungarella, M., Hafner, V., Pfeifer, R., and Yokoi, H. (2002). Artificial whisker sensors in robotics. Intelligent Robots and System, 2002. IEEE/RSJ International Conference on, 3:2931- 2936.
  20. Meyer, J.-A., Guillot, A., Girard, B., Khamassi, M., Pirim, P., and Berthoz, A. (2005). The psikharpax project: Towards building an artificial rat. Robotics and Autonomous Systems, 50(4):211-223.
  21. Moore, C. I. and Andermann, M. L. (2005). The Vibrissa Resonance Hypothesis, chapter 2, pages 21-60. CRC Press.
  22. Neimark, M. A., Andermann, M. L., Hopfield, J. J., and Moore, C. I. (2003). Vibrissa resonance as a transduction mechanism for tactile encoding. The Journal of Neuroscience.
  23. N'Guyen, S., Pirim, P., and Meyer, J.-A. (2009). Elastomerbased tactile sensor array for the artificial rat psikharpax. In ISEF 2009 - XIV International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering. In press.
  24. Nissen, S. (2003). Implementation of a Fast Artificial Neural Network Library (fann). Report, Department of Computer Science University of Copenhagen (DIKU), 31.
  25. Petersen, R. S. and Diamond, M. E. (2000). SpatialTemporal Distribution of Whisker-Evoked Activity in Rat Somatosensory Cortex and the Coding of Stimulus Location. J. Neurosci., 20(16):6135-6143.
  26. Russell, R. A. (1985). Object recognition using articulated whisker probes. In Proc. 15th Int. Symp. Industr. Robots., pages 605-612.
  27. Seth, A. K., McKinstry, J. L., Edelman, G. M., and Krichmar, J. L. (2004). Spatiotemporal processing of whisker input supports texture discrimination by a brain-based device. In Schall, S., Ijspeert, A., Billard, A., Vijayakumar, S., Hallam, J., and Meyer, J., editors, From Animals to Animats 8. Proc. of the 8th Int. Conf. on Simulation of Adaptive Behavior. MA: MIT PRESS.
  28. Sreenivas, T. V. and Niederjohn, R. J. (1992). Spectral analysis for formant frequency estimation in noise. IEEE Transactions on Signal Processing, 40(2):282-293.
Download


Paper Citation


in Harvard Style

N'Guyen S., Pirim P. and Meyer J. (2010). TACTILE TEXTURE DISCRIMINATION IN THE ROBOT-RAT PSIKHARPAX . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 74-81. DOI: 10.5220/0002730200740081


in Bibtex Style

@conference{biosignals10,
author={Steve N'Guyen and Patrick Pirim and Jean-Arcady Meyer},
title={TACTILE TEXTURE DISCRIMINATION IN THE ROBOT-RAT PSIKHARPAX},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={74-81},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002730200740081},
isbn={978-989-674-018-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - TACTILE TEXTURE DISCRIMINATION IN THE ROBOT-RAT PSIKHARPAX
SN - 978-989-674-018-4
AU - N'Guyen S.
AU - Pirim P.
AU - Meyer J.
PY - 2010
SP - 74
EP - 81
DO - 10.5220/0002730200740081