Learning with a Quadruped Chopstick Robot

  • Wei-Chung Lee
  • Jong-Chen Chen
  • Shou-zhe Wu
  • Kuo-Ming Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5632)


Organisms exhibit a close structure-function relationship and a slight change in structure may in turn change their outputs accordingly [1]. This feature is important as it is the main reason why organisms have better malleability than computers in dealing with environmental changes. A quadruped chopstick robot controlled by a biologically-motivated neuromolecular model, named Miky, has been developed. Miky’s skeleton and its four feet were comprised of 16 deposable chopsticks, with each foot being controlled by an actuator (motor). The neuromolecular model is a multilevel neural network which captures the biological structure-function relationship and serves to transform signals sent from its sensors into a sequence of signals in space and time for controlling Miky’s feet (through actuators). The task is to teach Miky to walk, jump, pace, gallop, or make a turn. Our experimental result shows that Miky exhibits a close structure-function relationship that allows it to learn to accomplish these tasks in a continuous manner.


Evolutionary learning Robot Neural networks Sensors 


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  1. 1.
    Conrad, M.: Bootstrapping on the adaptive landscape. BioSystems 11, 167–182 (1979)CrossRefGoogle Scholar
  2. 2.
    Conrad, M.: The geometry of evolution. BioSystem 24, 61–81 (1990)CrossRefGoogle Scholar
  3. 3.
    de Garis, H.: An artificial brain: ATR’s cam-brain project aims to build/evolve an artificial brain with a million neural net modules inside a trillion cell cellular automata machine. New Generation Computing Journal 12, 2 (1994)Google Scholar
  4. 4.
    Nam, D., Seo, Y.D., Park, L.-J., Park, C.H., Kim, B.: Parameter optimization of an on-robot voltage reference circuit using evolutionary programming. IEEE Trans. Evol. Comput. 5(4), 414–421 (2001)CrossRefGoogle Scholar
  5. 5.
    Higuchi, T., Iwata, M., Keymeulen, D., Sakanashi, H., Murakawa, M., Kajitani, I., Takahashi, E., Toda, K., Salami, M., Kajihara, N., Otsu, N.: Real-world applications of analog and digital evolvable hardware. IEEE Trans. Evol. Comput. 3(3), 220–235 (1999)CrossRefGoogle Scholar
  6. 6.
    Thompson, A.: Evolving electronic robot controllers that exploit hardware resources. In: Proc. 3rd European Conf. Artificial Life, Granada, Spain, pp. 640–656 (1995)Google Scholar
  7. 7.
    Miller, J.F., Downing, K.: Evolution in materio: looking beyond the silicon box. In: Proc. NASA/DoD Conf. Evolvable Hardware, pp. 167–176 (2002)Google Scholar
  8. 8.
    Vassilev, V.K., Job, D., Miller, J.F.: Towards the automatic design of more efficient digital circuits. In: Proc. 2nd NASA/DoD Workshop on Evolvable Hardware, Palo Alto, CA, pp. 151–160 (2000)Google Scholar
  9. 9.
    Thompson, A., Layzell, P.: Analysis of unconventional evolved electronics. Comm. ACM 42(4), 71–79 (1999)CrossRefGoogle Scholar
  10. 10.
    Chen, J.-C., Conrad, M.: Learning synergy in a multilevel neuronal architecture. BioSystems 32(2), 111–142 (1994)CrossRefGoogle Scholar
  11. 11.
    Liberman, E.A., Minina, S.V., Shklovsky-Kordy, N.E., Conrad, M.: Microinjection of cyclic nucleotides provides evidence for a diffusional mechanism of intraneuronal control. BioSystems 15, 127–132 (1982)CrossRefGoogle Scholar
  12. 12.
    Hameroff, S.R., Watt, R.C.: Information processing in microtubules. J. Theoretical Biology 98, 549–561 (1982)CrossRefGoogle Scholar
  13. 13.
    Matsumoto, G., Tsukita, S., Arai, T.: Organization of the axonal cytoskeleton: differentiation of the microtubule and actin filament arrays. In: Kinesin, D., Warner, F.D., McIntosh, J.R. (eds.) Cell Movement. Microtubule Dynamics, vol. 2, pp. 335–356. Alan R. Liss, New York (1989)Google Scholar
  14. 14.
    Werbos, P.: The cytoskeleton: why it may be crucial to human learning and to neurocontrol. Nanobiology 1, 75–95 (1992)Google Scholar
  15. 15.
    Conrad, M.: Molecular information processing in the central nervous system. In: Conrad, M., Gütinger, W., Dal Cin, M. (eds.) Physics and Mathematics of the Nervous System, pp. 82–127. Springer, Heidelberg (1974)CrossRefGoogle Scholar
  16. 16.
    Conrad, M.: Molecular information structures in the brain. J. Neurosci. Res. 2, 233–254 (1976)CrossRefGoogle Scholar
  17. 17.
    Eldredge, N., Gould, S.J.: Punctuated equilibria: an alternative to phyletic gradualism. In: Schopf, T.J.M. (ed.) Models in Paleobiology, pp. 82–115. Freeman, Cooper and Company, San Francisco (1972)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Wei-Chung Lee
    • 1
  • Jong-Chen Chen
    • 1
  • Shou-zhe Wu
    • 1
  • Kuo-Ming Lin
    • 1
  1. 1.National Yunlin University of Science and TechnologyTaiwan, R.O.C.

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