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An anthropomorphic model of sensory-motor co-ordination of manipulation for robots

  • Chapter 14 Learning & Skill Acquisition
  • Conference paper
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Abstract

This paper investigates the problem of artificial perception related to manipulation tasks in robotics.

The proposed approach is based on biological models of perception and sensory-motor co-ordination in humans and aims at devising anthropomorphic solutions to the problems of perception, learning and control in robotics. In particular, our approach involves the integration of different sensory modalities and the interpretation of sensory data aimed at the control of robot behaviour.

We consider as sensory modalities, in relation to manipulation tasks, vision and haptic perception, i.e. the integration of tactile proprioceptive and exteroceptive data.

The experimental part of this work is aimed at investigating some aspects of the proposed anthropomorphic model of perception in manipulation by means of anthropomorphic visual and tactile sensors on a robotic manipulator and a pantilt head, and a processing module based on neural network computational models integrated with the reinforcement learning paradigm.

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8 Bibliography

  1. Bekey G., Crisman J. 1996 The ‘grand challenge’ for robotics and automation, Special Panel discussion,IEEE Conference on Robotics and Automation, Minneapolis, Minnesota, USA, April 22–28

    Google Scholar 

  2. Proc. of the First International Symposium on HUmanoid RObots, 1996, sponsored by Waseda University, Tokyo, Japan, October 30–31

    Google Scholar 

  3. Brooks R. 1996 Prospects for human level intelligence for humanoid robots,Proc. of the First International Symposium on HUmanoid RObots, Waseda University, Tokyo, Japan, October 30–31, 17–24

    Google Scholar 

  4. Lee C.W 1996 Project “CENTAUR” in mid-entry strategy — KIST 2000 human robot system project,Proc. of the First International Symposium on HUmanoid Robots, Waseda University, Tokyo, Japan, October 30–31, 73–82

    Google Scholar 

  5. Yamaguchi J. 1996 Development of a humanoid robot — Design of a biped walking robot having antagonist driven joints using nonlinear spring mechanism,Proc. of the First International Symposium on HUmanoid Robots, Waseda University, Tokyo, Japan, October 30–31, 102–110

    Google Scholar 

  6. Albus J.S. 1981Brains, Behaviour & Robotics, BYTE Books, Subsidiary of Mc Graw Hill, Peterborough, N.H., USA

    Google Scholar 

  7. Sperry R.W. 1970 Perception in the absence of the Neocortical Commisures, inPerception and its Disorders, Research Publication of the Association for Research in Nervous and Mental Diseases 48.

    Google Scholar 

  8. Piaget J. 1976The grasp of consciousness: action and concept in the young child, Harvard University Press, Cambridge, MA, USA

    Google Scholar 

  9. Aloimonos J., Weiss I., Bandyopadhay A. 1988 Active Vision,International Journal of Computer Vision, 1:333–356

    Article  Google Scholar 

  10. Bajcsy R. 1988 Active Perception, inProc. of the IEEE, 76, 8:996–1005

    Article  Google Scholar 

  11. Rumhelhart D., Mc Clelland J. 1986Parallel Distributed Processing, Cambridge, Massachusetts, MIT Press

    Google Scholar 

  12. Haykin S. 1994Neural Networks: A Comprehensive Foundation, IEEE Comp. Spc. Press, McMillan

    MATH  Google Scholar 

  13. Dario P., Rucci M. 1993 A neural network-based robotic system implementing recent biological theories on tactile perception, inProc. of 3rdInternational Symposium on Experimental Robotics, Kyoto, Japan, 162–167

    Google Scholar 

  14. Rucci M., Dario P. 1994 Development of cutaneo-motor co-ordination in an autonomous robotic system,Autonomous Robots, 1:93–106

    Article  Google Scholar 

  15. Sutton R.S. 1996 Reinforcement Learning,NIPS Tutorial, Dec. 2

    Google Scholar 

  16. Meeden L.A. 1994, An incremental Approach to developing intelligent neural network controllers for robots

    Google Scholar 

  17. Watkins C.J.C.H. 1989 Learning from delayed rewards, Ph.D. Thesis, University of Cambridge, UK

    Google Scholar 

  18. Sutton R.S. 1988 Learning to predict by the methods of temporal differences, Machine Learning 3, 9–44

    Google Scholar 

  19. Shaal S., Atkenson C. 1994 Robot Juggling: an implementation of memory based learning,Control System Magazine, 14

    Google Scholar 

  20. Mataric M.J. 1994 Reward functions for accelerated learning, inProc. of the 11thInternational Conference on Machine Learning, Morgan Kaufmann

    Google Scholar 

  21. Crites R.H., Barto A.G. 1996 Improving elevator performance using reinforcement learning, in Touretzky D., Mozer M., Hasselmo M. editorsNeural Information Processing Systems 8

    Google Scholar 

  22. Morgan J.S., Patterson E.C., Klopf A.H. 1990 Drive-reinforcement learning: a self-supervised model for adaptive control,Network: Computation in Neural Systems, 1:439–448

    Article  Google Scholar 

  23. Touzet C. 1994Neural Implementations of immediate reinforcement learning for an obstacle avoidance behaviour, Technical Report NM.94.6, EERIE, Nimes, France

    Google Scholar 

  24. Shibata K., Nishino T., Okabe Y. 1995 Active perception based on reinforcement learning, inProc. of WCNN'95, Washington D.C., 2:170–173

    Google Scholar 

  25. Dario P., Guglielmelli E., Laschi C., Guadagnini C., Pasquarelli G., Morana G. 1995 MOVAID: a new European joint project in the field of rehabilitation robotics, inProc. of the 7thInternational Conference on Advanced Robotics (ICAR’ 95), Sant Feliu de Guíxols, Spain, September 20–22, 51–59

    Google Scholar 

  26. Dario P., Guglielmelli E., Laschi C., Teti G. 1997 MOVAID: a mobile robotic system for residential care to disabled and elderly people,Proc. of the 1stMobiNet Symposium, Athens, Greece, May 15–16, 1997, pp.45–68

    Google Scholar 

  27. Sandini G., Dario P., De Micheli M., Tistarelli M. 1993 Retina-like CCD sensor for active vision, inRobots and Biological Systems, Dario P., Sandini G., Aebischer P. Ed.s, Berlin-Heidelberg: Springer-Verlag, 553–570

    Google Scholar 

  28. Dario P., Lazzarini R., Magni R., Oh S.R., 1996 An integrated miniature fingertip sensor, inProc. of the 7thInternational Symposium on Micro Machine and Human Science (MHS’ 96), Nagoya, Japan, October 2–4

    Google Scholar 

  29. Allotta B., Bosio L., Chiaverini S., Guglielmelli E. 1993 A redundant arm for the URMAD robot unit, inProc. of the 6thInternational Conference of Advanced Robotics (ICAR’ 93), Tokyo, Japan, November 1–2, 655–660

    Google Scholar 

  30. Allotta B., Bioli G., Colla V. 1996 A repeatable control scheme for redundant manipulators observing joint mechanical limits, inProc. of Robotics towards 2000: 27thInternational Symposium on Industrial Robots, Milan, Italy, October 6–8, 521–526

    Google Scholar 

  31. Hough P.V.C. 1962 Method and means for recognising complex patterns, U.S. Patent 3069654, December 18th

    Google Scholar 

  32. Williams R.J. 1992 Simple statistical gradient-following algorithm for connectionist reinforcement learning,Machine Learning, 8:229–256

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Scuola Superiore Sant’Anna, Pisa, Italy

    Cecilia Laschi, Davide Taddeucci & Paolo Dario

Authors
  1. Cecilia Laschi

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  2. Davide Taddeucci

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  3. Paolo Dario

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Editor information

Alicia Casals Anibal T. de Almeida

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© 1998 Springer-Verlag London Limited

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Laschi, C., Taddeucci, D., Dario, P. (1998). An anthropomorphic model of sensory-motor co-ordination of manipulation for robots. In: Casals, A., de Almeida, A.T. (eds) Experimental Robotics V. Lecture Notes in Control and Information Sciences, vol 232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0113000

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