Learning to move a robot with random morphology

  • Peter Dittrich
  • Andreas Bürgel
  • Wolfgang Banzhaf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1468)


Complex robots inspired by biological systems usually consist of many dependent actuators and are difficult to control. If no model is available automatic learning and adaptation methods have to be applied. The aim of this contribution is twofold: (1) To present an easy to maintain and cheap test platform, which fulfils the requirements of a complex control problem. (2) To discuss the application of Genetic Programming for evolution of control programs in real time. An extensive number of experiments with two real robots has been carried out.


genetic programming real-time robotics random morphology robot hardware evolution 


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  1. 1.
    Sims, K.: Evolving 3D Morphology and Behavior by Competition, Proceedings of the 4th International Workshop on the Synthesis and Simulation of Living Systems, Artificial Life IV, pp. 28–39, MIT Press, July (1994)Google Scholar
  2. 2.
    Paap, K. L., Dehlwisch, M., Klaassen, B.: GMD-Snake: A Semi-Autonomous Snakelike Robot, In: Distributed Autonomous Robotic Systems 2, Springer-Verlag, Tokio, (1996)Google Scholar
  3. 3.
    Ostrowski J. P., Burdick, J. W.: Gait Kinematics for a Serpentine Robot, Int. Conf. on Robotics and Automation (1996)Google Scholar
  4. 4.
    Triantafyllou, M. S., Triantafyllo, G. S.: An efficient swimming machine, Scientific American, 272, pp. 64–70 (1995), see also: Scholar
  5. 5.
    Banzhaf, W., Nordin P., Keller, R. E., Francone F.: Genetic Programming — an Introduction Morgan Kaufmann, (1997)Google Scholar
  6. 6.
    Mataric, M. J., Cliff. D.: Challenges in evolving controllers for physical robots. Journal of Robotics and Autonomous Systems 19(1), 67–83 (1996).Google Scholar
  7. 7.
    Davidor, Y.: Genetic Algorithms and Robotics. World Scientific, Singapore, 1990.Google Scholar
  8. 8.
    Dorigo, M., Schneph, U.: Genetics-based machine learning and behavior based robotics. IEEE Transactions on Systems, Man and Cybernetics, 23(1), 1993.Google Scholar
  9. 9.
    Koza. J. R.: Evolution of subsumption using genetic programming. In F. J. Varela and P. Bourgine, editors, Proceedings of the First European Conference on Artificial Life. Towards a Practice of Autonomous Systems, pages 110–119, Paris, France, 11–13 December 1992. MIT Press.Google Scholar
  10. 10.
    Koza, J. R.: Genetic Programming, MIT Press, Cambridge MA, (1992)Google Scholar
  11. 11.
    Lee, W.-P. Hallam, J., Lund. H. H.: Learning complex robot behaviors by evolutionary approaches. In 6th European Workshop on Learning Robots, EWLR-6, pages 42–51, Hotel Metropole, Brighton, UK, 1–2 August 1997.Google Scholar
  12. 12.
    Nordin, P., Banzhaf, W.: Genetic programming controlling a miniature robot. In E. V. Siegel and J. R. Koza, editors, Working Notes for the AAAI Symposium on Genetic Programming, pages 61–67, MIT, Cambridge, MA, USA, 10–12 November 1995. AAAI.Google Scholar
  13. 13.
    Olmer, M., Banzhaf, W., Nordin. P.: Evolving real-time behavior modules for a real robot with genetic programming. In Proceedings of the international symposium on robotics and manufacturing, Montpellier, France, May 1996.Google Scholar
  14. 14.
    Singleton, A.: gpquick (Steady-state tree-based C++ GP-Sytem), ftp. Scholar
  15. 15.
    Salomon. R.: Scaling behavior of the evolution srategy when evolving neuronal control architectures for autonomous agents. In Evolutionary Programming 6 6th International Conference, EP97, pages 48–57, Indianapolis, Indiana, USA, apr 1997.Google Scholar
  16. 16.
    Steels, L.: Emergent functionality in robotic agents through on-line evolution. In Rodney A. Brooks and Pattie Maes, editors, Proceedings of the 4th International Workshop on the Synthesis and Simulation of Living Systems Arti f icial Li f eIV, pages 8–16, Cambridge, NIA, USA, July 1994. MIT Press.Google Scholar
  17. 17.
    Nolfi, S., Floreano, D., Miglino, O., Mondada. F.: How to evolve autonomous robots: Different approaches in evolutionary robotics. In Rodney A. Brooks and Pattie Maes, editors, Proceedings of the 4th International Workshop on the Synthesis and Simulation of Living Systems Artificial Life lV, pages 190–197, Cambridge, MA, USA, July 1994. MIT Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Peter Dittrich
    • 1
  • Andreas Bürgel
    • 1
  • Wolfgang Banzhaf
    • 1
  1. 1.Dept. of Computer ScienceUniversity of DortmundDortmundGermany

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