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Morphological Computation – Connecting Brain, Body, and Environment

  • Rolf Pfeifer
  • Gabriel Gómez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5436)

Abstract

Traditionally, in robotics, artificial intelligence, and neuroscience, there has been a focus on the study of the control or the neural system itself. Recently there has been an increasing interest in the notion of embodiment not only in robotics and artificial intelligence, but also in neuroscience, psychology, and philosophy. In this paper, we introduce the notion of morphological computation and demonstrate how it can be exploited on the one hand for designing intelligent, adaptive robotic systems, and on the other for understanding natural systems. While embodiment has often been used in its trivial meaning, i.e. “intelligence requires a body”, the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. Morphological computation is about connecting body, brain and environment. A number of case studies are presented to illustrate the concept. We conclude with some speculations about potential lessons for neuroscience and robotics, in particular for building brain-like intelligence, and we present a theoretical scheme that can be used to embed the diverse case studies.

Keywords

Embodiment sensor morphology material properties information self-structuring morphological change dynamics system- environment interaction 

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References

  1. 1.
    Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (1999)Google Scholar
  2. 2.
    Pfeifer, R., Bongard, J.: How the body shapes the way we think. MIT Press, Cambridge (2007)Google Scholar
  3. 3.
    Markram, H.: The blue brain project. Nature Reviews — Neuroscience 7, 153–159 (2006)CrossRefPubMedGoogle Scholar
  4. 4.
    Lungarella, M.: Exploring principles towards a developmental theory of embodied artificial intelligence. Ph.D. dissertation, University of Zurich, Switzerland (2004)Google Scholar
  5. 5.
    Lungarella, M., Pegors, T., Bulwinkle, D., Sporns, O.: Methods for quantifying the information structure of sensory and motor data. Neuroinformatics 3(3), 243–262 (2005)CrossRefPubMedGoogle Scholar
  6. 6.
    Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science 318, 1088–1093 (2007)CrossRefPubMedGoogle Scholar
  7. 7.
    Lichtensteiger, L.: On the interdependence of morphology and control for intelligent behavior. Ph.D. dissertation, University of Zurich (2004)Google Scholar
  8. 8.
    Pfeifer, R.: On the role of morphology and materials in adaptive behavior. In: Sixth International Conference on Simulation of Adaptive Behavior (SAB), pp. 23–32 (2000)Google Scholar
  9. 9.
    Pfeifer, R.: Morpho-functional machines: basics and research issues. In: Morpho-functional machines: the new species. Springer, Tokyo (2003)Google Scholar
  10. 10.
    Franceschini, N., Pichon, J.M., Blanes, C.: From insect vision to robot vision. Philos. Trans. R. Soc. London B. 337, 283–294 (1992)CrossRefGoogle Scholar
  11. 11.
    Hoshino, K., Mura, F., Shimoyama, I.: Design and performance of a micro-sized biomorphic compound eye with a scanning retina. Journal of Microelectromechanical Systems 9, 32–37 (2000)CrossRefGoogle Scholar
  12. 12.
    Yokoi, H., Arieta, A.H., Katoh, R., Yu, W., Watanabe, I., Maruishi, M.: Mutual adaptation in a prosthetics application. In: [47], pp. 147–159 (2004)Google Scholar
  13. 13.
    Molina-Vilaplana, J., Feliu-Batlle, J., López-Coronado, J.: A modular neural network architecture for step-wise learning of grasping tasks. Neural Networks 20(5), 631–645 (2007)CrossRefPubMedGoogle Scholar
  14. 14.
    Takamuku, S., Gómez, G., Hosoda, K., Pfeifer, R.: Haptic discrimination of material properties by a robotic hand. In: 6th IEEE International Conference on Development and Learning, ICDL (in press, 2007) (accepted for publication)Google Scholar
  15. 15.
    Gómez, G., Hernandez, A., Eggenberger Hotz, P.: An adaptive neural controller for a tendon driven robotic hand. In: Arai, T., Pfeifer, R., Balch, T., Yokoi, H. (eds.) Proceedings of the 9th International Conference on Intelligent Autonomous Systems (IAS-9), Tokyo, Japan, pp. 298–307. IOS Press, Amsterdam (2006)Google Scholar
  16. 16.
    Gómez, G., Hotz, P.E.: Evolutionary synthesis of grasping through self-exploratory movements of a robotic hand. In: IEEE Congress on Evolutionary Computation (CEC 2007) (2007)Google Scholar
  17. 17.
    Borst, C., Fischer, M., Hirzinger, G.: Calculating hand configurations for precision and pinch grasps. In: IEEE/RSJ Int. Conference on Intelligent robots and Systems (IROS 2002), vol. 2, pp. 1553–1559 (2002)Google Scholar
  18. 18.
    Yu, W., Yokoi, H., Kakazu, Y.: Focus on Robotics Research. In: Liu, J.X. (ed.) An Interaction Based Learning Method for Assistive Device Systems, pp. 123–159. Nova Publishers (2006)Google Scholar
  19. 19.
    Hernandez, A., Katoh, R., Yokoi, H., Yu, W.: Development of a multi-dofelectromyography prosthetic system using the adaptive joint mechanism. Applied Bionics and Biomechanics 3(2), 101–112 (2006)CrossRefGoogle Scholar
  20. 20.
    Lungarella, M., Sporns, O.: Mapping information flow in sensorimotor networks. PLoS Comp. Biol. 2, e144 (2006)CrossRefGoogle Scholar
  21. 21.
  22. 22.
  23. 23.
    Staudacher, E.: Distribution and morphology of descending brain neurons in the cricket gryllus bimaculatus. Cell Tisues Res. 294, 187–202 (1998)CrossRefGoogle Scholar
  24. 24.
    Watson, J., Ritzmann, R.: Leg kinematics and muscle activity during treadmill running in the cockroach, blaberus discoidalis: I. slow running. J. Comp. Physiol. A 182, 11–22 (1998)CrossRefPubMedGoogle Scholar
  25. 25.
    Watson, J., Ritzmann, R., Pollack, A.: Control of climbing behavior in the cockroach, blaberus discoidalis. ii. motor activities associated with joint movement. J. Comp. Physiol. A 188, 55–69 (2002)CrossRefGoogle Scholar
  26. 26.
    McGeer, T.: Passive dynamic walking. The International Journal of Robotics Research 9(2), 62–82 (1990)CrossRefGoogle Scholar
  27. 27.
    McGeer, T.: Passive walking with knees. In: IEEE Conference on Robotics and Automation, vol. 2, pp. 1640–1645 (1990)Google Scholar
  28. 28.
    Collins, S., Ruina, A., Tedrake, R., Wisse, M.: Efficient bipedal robots based on passive dynamic walkers. Science 307, 1082–1085 (2005)CrossRefPubMedGoogle Scholar
  29. 29.
    Wisse, M.: Three additions to passive dynamic walking: actuation, an upper body, and 3d stability. International Journal of Humanoid Robotics 2(4), 459–478 (2005)CrossRefGoogle Scholar
  30. 30.
    Takuma, T., Hosoda, H.: Controlling the walking period of a pneumatic muscle walker. The International Journal of Robotics Research 25(9), 861–866 (2006)CrossRefGoogle Scholar
  31. 31.
    Paul, C., R., Dravid, F.: Control of lateral bounding for a pendulum driven hopping robot. In: International Conference of Climbing and Walking Robots, Paris, France. (2002)Google Scholar
  32. 32.
    Paul, C., R., Dravid, F.: Design and control of a pendulum driven hopping robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2002, Lausanne, Switzerland (2002)Google Scholar
  33. 33.
    Iida, F., Pfeifer, R.: “cheap” rapid locomotion of a quadruped robot: Self-stabilization of bounding gait. In: Groen, F., Amato, N., Bonarini, A., Yoshida, E., Krse, B. (eds.) Proc. of the 8th Int. Conf. on Intelligent Autonomous Systems (IAS-8), pp. 642–649. IOS Press, Amsterdam (2004)Google Scholar
  34. 34.
    Iida, F., Pfeifer, R.: Sensing through body dynamics. Robotics and Autonomous Systems 54 (8), 631–640 (2006)CrossRefGoogle Scholar
  35. 35.
    Kubow, T.M., Full, R.J.: The role of the mechanical system in control: a hypothesis of self-stabilization in hexapedal runners. Philosophical Transactions of the Royal Society B. 354, 849–861 (1999)CrossRefGoogle Scholar
  36. 36.
    Blickhan, R., Wagner, H., Seyfarth, A.: Brain or muscles? Rec. Res. Devel. Biomechanics 1, 215–245 (2003)Google Scholar
  37. 37.
    Iida, F.: Cheap design and behavioral diversity for autonomous adaptive robots. Ph.D. dissertation, Faculty of Mathematics and Science, University of Zurich, Switzerland (2005)Google Scholar
  38. 38.
    Buehler, M.: Dynamic locomotion with one, four and six-legged robots. J. of the Rob. Soc. of Japan 20(3), 15–20 (2002)Google Scholar
  39. 39.
    Schmitz, A., Gómez, G., Iida, F., Pfeifer, R.: Adaptive control of dynamic legged locomotion. In: Concept Learning Workshop, IEEE International Conference on Robotics and Automation (ICRA 2007) (2007)Google Scholar
  40. 40.
    Schmitz, A., Gómez, G., Iida, F., Pfeifer, R.: On the robustness of simple speed control for a quadruped robot. In: Proceedings of International Conference on Morphological Computation Workshop 2007, pp. 88–90 (2007)Google Scholar
  41. 41.
    Rinderknecht, M., Ruesch, J., Hadorn, M.: The lagging legs exploiting body dynamics to steer a quadrupedal agent. In: International Conference on Morphological Computation, Venice, Italy (2007)Google Scholar
  42. 42.
    Cruse, H.: What mechanisms coordinate leg movement in walking arthropods? Trends in Neurosciences 13, 15–21 (1990)CrossRefPubMedGoogle Scholar
  43. 43.
    Cruse, H., Dean, J., Durr, V., Kindermann, T., Schmitz, J., Schumm, M.: A decentralized, biologically based network for autonomous control of (hexapod) walking. In: Neurothecnology for biomimetic robots, pp. 384–400. MIT Press, Cambridge (2002)Google Scholar
  44. 44.
    Dür, V., Krause, A.F., Schitz, J., Cruse, H.: Neuroethological concepts and their transfer to walking machines. International Journal of Robotics Research 22(3-4), 151–167 (2003)CrossRefGoogle Scholar
  45. 45.
    Ziegler, M., Iida, F., Pfeifer, R.: Cheap underwater locomotion: Morphological properties and behavioral diversity. In: IROS 2005 Workshop on Morphology, Control, and Passive Dynamics (2005)Google Scholar
  46. 46.
    Pfeifer, R., Iida, F., Gómez, G.: Morphological computation for adaptive behavior and cognition. International Congress Series 1291, 22–29 (2006)CrossRefGoogle Scholar
  47. 47.
    Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y. (eds.): Embodied Artificial Intelligence. LNCS, vol. 3139, pp. 1–26. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rolf Pfeifer
    • 1
  • Gabriel Gómez
    • 2
  1. 1.Artificial Intelligence Laboratory, Department of InformaticsUniversity of ZurichZurichSwitzerland
  2. 2.Humanoid Robotics GroupCSAIL, MITCambridgeUSA

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