Advertisement

Autonomous Robots

, Volume 25, Issue 1–2, pp 85–101 | Cite as

A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping

  • Cecilia Laschi
  • Gioel Asuni
  • Eugenio Guglielmelli
  • Giancarlo Teti
  • Roland Johansson
  • Hitoshi Konosu
  • Zbigniew Wasik
  • Maria Chiara Carrozza
  • Paolo Dario
Article

Abstract

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image.

The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.

Keywords

Predictive control Sensory-motor coordination Robot grasping Robot learning Expected perception Internal models Neuro-fuzzy controllers 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arbib, M. A., & Iberall, T. M. L. D. (1985). Coordinated control programs for movements of the hand. In A. W. Goodwin & I. Darian-Smith (Eds.), Hand function and the neocortex (pp. 111–129). Berlin: Springer. Google Scholar
  2. Beer, R. D., Quinn, R. D., Chiel, H. J., & Ritzmann, R. E. (1997). Biologically inspired approaches to robotics. Communications of the ACM, 40(3), 30–38. CrossRefGoogle Scholar
  3. Bekey, G. A., & Tomovic, R. (1990). Biologically based robot control. In Proceedings of the annual international conference of the IEEE engineering in medicine and biology society (Vol. 12, pp. 1938–1939). Google Scholar
  4. Bekey, G. A., Liu, H., Tomovic, R., & Karplus, W. J. (1993). Knowledge-based control of grasping in robot hands using heuristics from human motor skills. IEEE Transactions on Robotics and Automation, 9, 709–722. CrossRefGoogle Scholar
  5. Berthoz, A. (1997). Le Sens Du Mouvement. Paris: O. Jacob. Google Scholar
  6. Bicchi, A., & Kumar, V. (2000). Robotic grasping and contact: A review. In Proceedings of the conference on robotics and automation, San Francisco (pp. 348–353). Google Scholar
  7. Brice, C. L., & Fennema, C. R. (1970). Scene analysis using regions. Artificial Intelligence, 1, 205–226. CrossRefGoogle Scholar
  8. Brooks, R. A. (1991). New approaches to robotics. Science, 253, 1227–1232. CrossRefGoogle Scholar
  9. Carrozza, M. C., Vecchi, F., Sebastiani, F., Cappiello, G., Roccella, S., Zecca, M., Lazzarini, R., & Dario, P. (2003). Experimental analysis of an innovative prosthetic hand with proprioceptive sensors. In Proceedings of the IEEE international conference on robotics and automation (pp. 2230–2235). Google Scholar
  10. Charlebois, M., Gupta, K., & Payandeh, S. (1999). Shape description of curved surfaces from contact sensing using surface normals. International Journal of Robotics Research, 18(8), 779–787. CrossRefGoogle Scholar
  11. Cutkosky, M. R., & Howe, R. D. (1990). Human grasp choice and robotic grasp analysis. In S. T. Venkataraman & T. Iberall (Eds.), Dextrous robot hands (pp. 5–31). New York: Springer. Google Scholar
  12. Dario, P., Carrozza, M. C., Guglielmelli, E., Laschi, C., Menciassi, A., Micera, S., & Vecchi, F. (2005). Robotics as a ‘future and emerging technology’: Biomimetics, cybernetics and neuro-robotics in European projects. IEEE Robotics and Automation Magazine, 12(2), 29–43. CrossRefGoogle Scholar
  13. Datteri, E., Teti, G., Laschi, C., Tamburrini, G., Dario, P., & Guglielmelli, E. (2003a). Expected perception: An anticipation-based perception-action scheme in robots. In IROS 2003, 2003 IEEE/RSJ international conference on intelligent robots and systems, Las Vegas, Nevada (pp. 934–939). Google Scholar
  14. Datteri, E., Teti, G., Laschi, C., Tamburrini, G., Dario, P., & Guglielmelli, E. (2003b). Expected perception in robots: A biologically driven perception-action scheme. In Proceedings of ICAR 2003, 11th international conference on advanced robotics (Vol. 3, pp. 1405–1410). Google Scholar
  15. Datteri, E., Asuni, G., Teti, G., Laschi, C., & Guglielmelli, E. (2004). Experimental analysis of the conditions of applicability of a robot sensorimotor coordination scheme based on expected perception. In Proceedings of 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS), Sendai, Japan (Vol. 2, pp. 1311–1316). Google Scholar
  16. Fearing, R. S. (1990). Tactile sensing for shape interpretation. In S. T. Venkataraman & T. Iberall (Eds.), Dextrous robot hands (pp. 209–238). New York: Springer. Google Scholar
  17. Guglielmelli, E., Asuni, G., Leoni, F., Starita, A., & Dario, P. (2007, in press). A neuro-controller for robotic manipulators based on biologically-inspired visuo-motor co-ordination neural models. In Handbook of neural engineering : Vol. 26. Neural engineering series (pp. 433–448). New York: Wiley/IEEE Press. Google Scholar
  18. Haykin, S. (1994). Neural networks, a comprehensive foundation (2nd edn.). New York: Prentice Hall. MATHGoogle Scholar
  19. Hong, W., & Slotine, J. J. E. (1995). Experiments in hand-eye coordination using active vision. In Proceedings of the fourth international symposium on experimental robotics, Stanford, CA. Google Scholar
  20. Iberall, T., & MacKenzie, C. L. (1990). Opposition space and human prehension. In S. T. Venkataraman & T. Iberall (Eds.), Dextrous robot hands (pp. 32–54). New York: Springer. Google Scholar
  21. Jeannerod, M. (1984). The timing of natural prehension movements. Journal of Motor Behavior, 16(3), 235–254. Google Scholar
  22. Jeannerod, M., Paulignan, Y., & Weiss, P. (1998). Grasping an object: One movement, several components. In Novartis found symposium (Vol. 218, pp. 5–20). Google Scholar
  23. Johansson, R. S. (1998). Sensory input and control of grip. In M. Glickstein (Ed.), Sensory guidance of movements (pp. 45–59). Chichester: Wiley. CrossRefGoogle Scholar
  24. Johansson, R. S., & Westling, G. (1987). Signals in tactile afferents from the fingers eliciting adaptive motor responses during precision grip. Experimental Brain Research, 66, 141–154. CrossRefGoogle Scholar
  25. Kawato, M. (1999). Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9, 718–727. Elsevier Science CrossRefGoogle Scholar
  26. Klatzky, R. L., & Lederman, S. J. (1990). Intelligent exploration by the human hand. In S. Venkataraman & T. Iberall (Eds.), Dextrous hands for robots (pp. 66–81). New York: Springer. Google Scholar
  27. Koivo, A. J. (1991). Real-time vision feedback for servoing robotic manipulator with self-tuning controller. IEEE Transactions on Systems, Man, and Cybernetics, 21(1), 134–142. CrossRefMathSciNetGoogle Scholar
  28. Kragic, D., & Christensen, H. I. (2002). Model based techniques for robotic servoing and grasping. In Proceedings of the 2002 IEEE/RSJ international conference on intelligent robots and systems, EPFL, Lausanne, Switzerland (pp 299–304). Google Scholar
  29. Laschi, C., Patanè, F., Maini, E. S., Manfredi, L., Teti, G., Zollo, L., Guglielmelli, E., & Dario, P. (2008). An anthropomorphic robotic head for investigating gaze control. Advanced Robotics, 22(1). Google Scholar
  30. Mason, M., & Salisbury, K. (1985). Robot hands and the mechanics of manipulation. Cambridge: MIT Press. Google Scholar
  31. Miall, R. C., Weir, D. J., Wolpert, D. M., & Stein, J. F. (1993). Is the cerebellum a smith predictor? Journal of Motor Behaviour, 25, 203–216. CrossRefGoogle Scholar
  32. Namiki, A., & Ishikawa, M. (2003). Robotic catching using a direct mapping from visual information to motor command. In Proceedings of the IEEE international conference on robotics and automation (pp. 2400–2405). Google Scholar
  33. Napier, J. R. (1956). The prehensile movements of the human hand. Journal of Bone and Joint Surgery, 36B(4), 902–913. Google Scholar
  34. Narasimhan, S., Spiegel, D. M., & Hollerbach, J. M. (1990). Condor: A computational architecture for robots. In S. T. Venkataraman & T. Iberall (Eds.), Dextrous robot hands (pp. 117–135). New York: Springer. Google Scholar
  35. Shimoga, K. B. (1996). Robot grasp synthesis algorithms: A survey. The International Journal of Robotics Research (MIT Press). Google Scholar
  36. Sobel, I. (1978). Neighbourhood coding of binary images fast contour following and general array binary processing. Computer Graphics and Image Processing, 8, 127–135. CrossRefGoogle Scholar
  37. Venkataraman, S. T., & Iberall, T. (Eds.). (1990). Dextrous robot hands. New York: Springer. Google Scholar
  38. Wang, J. S., Lee, C. S. G., & Juang, C. H. (Eds.). (1999). Structure and learning in self-adaptive neural fuzzy inference systems. In Proceedings of the eighth int’l fuzzy syst. Association world congress (IFSA’99), Taipei, Taiwan (pp. 975–980). Google Scholar
  39. Wolpert, D. M., Miall, R. C., & Kawato, M. (1998). Internal models in the cerebellum. Trends in Cognitive Sciences, 2(9), 338–347. CrossRefGoogle Scholar
  40. Wunsch, P., Winkler, S., & Hirzinger, G. (1997). Real-time pose estimation of 3-d objects from camera images using neural networks. In Proceedings of the 1997 IEEE international conference on robotics and automation (pp. 3232–3237). Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Cecilia Laschi
    • 1
  • Gioel Asuni
    • 1
  • Eugenio Guglielmelli
    • 2
  • Giancarlo Teti
    • 1
  • Roland Johansson
    • 3
  • Hitoshi Konosu
    • 4
  • Zbigniew Wasik
    • 4
  • Maria Chiara Carrozza
    • 1
  • Paolo Dario
    • 1
  1. 1.ARTS (Advanced Robotics Technology and Systems) LabScuola Superiore Sant’AnnaPisaItaly
  2. 2.CIR—Center for Integrated Research, Laboratory of Biomedical Robotics and BiomicrosystemsCampus-Biomedico UniversityRomeItaly
  3. 3.Umeå UniversityUmeåSweden
  4. 4.Toyota Motor EuropeBrusselsBelgium

Personalised recommendations