Gait Evolution for Humanoid Robot in a Physically Simulated Environment

Part of the Studies in Computational Intelligence book series (SCI, volume 374)


This article describes a bio-inspired system and the associated series of experiments, for the evolution of walking behavior in a simulated humanoid robot. A previous study has demonstrated the potential of this approach for evolving controllers based on simulated humanoid robots with a restricted range of movements. The development of anthropomorphic bipedal locomotion is addressed by means of artificial evolution using a genetic algorithm. The proposed task is investigated using full rigid-body dynamics simulation of a bipedal robot with 15 degrees of freedom. Stable bipedal gait with a velocity of 0.94 m/s is realized. Locomotion controllers are evolved from scratch, for example neither does the evolved controller have any a priori knowledge on how to walk, nor does it have any information about the kinematics structure of the robot. Instead, locomotion control is achieved based on intensive use of sensory information. In this work, the emergence of non-trivial walking behaviors is entirely due to evolution.


Recurrent Neural Network Humanoid Robot Bipedal Robot Locomotion Control Linear Genetic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Azarbadegan, A., Broz, F., Nehaniv, C.L.: Evolving Simss Creatures for Bipedal Gait. In: IEEE Symposium on Artificial Life, Paris, France, April 11-15. Symposium series on computational intelligence, pp. 218–224 (2011)Google Scholar
  2. 2.
    Cheng, M.Y., Lin, C.S.: Genetic algorithm for control design of biped locomotion. Journal of Robotic Systems 14(5), 365–373 (1997)zbMATHCrossRefGoogle Scholar
  3. 3.
    Clune, J., Ofria, C., Pennock, R.T.: How a generative encoding fares as problem regularity decreases. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 358–367. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Clune, J., Beckmann, B.E., Ofria, C., Pennock, R.T.: Evolving coordinated quadruped gaits with the hyperneat generative encoding. In: IEEE Congress on Evolutionary Computing (CEC), Trondheim, Norway, pp. 2674–2771 (2009)Google Scholar
  5. 5.
    Doerschuk, P.I., Simon, W.E., Nguyen, V., Li, A.: A Modular Approach to Intelligent Control of a Simulated Jointed Leg. IEEE Robotics and Automation Magazine 5(2), 12–21 (1998)CrossRefGoogle Scholar
  6. 6.
    Gallagher, J.C., Beer, R.D., Espenschied, K.S., Quinn, R.D.: Application of evolved locomotion controllers to a hexapod robot. Robotics and Autonomous Systems 19(1), 95–103 (1996)CrossRefGoogle Scholar
  7. 7.
    Elman, L.J.: Finding Structure in Time. Cognitive Science 14, 179–211 (1990)CrossRefGoogle Scholar
  8. 8.
    Gruau, F.: Automatic definition of modular neural networks. Adaptive Behavior 3(2), 151–183 (1995)CrossRefGoogle Scholar
  9. 9.
    Haykin, S.: Neural Networks: A comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  10. 10.
    Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)Google Scholar
  11. 11.
    Hornby, G.S., Lipson, H., Pollack, J.B.: Generative representations for the automated design of modular physical robots. IEEE Transactions on Robotics and Automation 19, 703–719 (2003)CrossRefGoogle Scholar
  12. 12.
    Hornby, G., Takamura, S., Tamamoto, T., Fujita, M.: Autonomous evolution of dynamic gaits with two quadruped robots. IEEE Transactions on Robotics 21(3), 402–410 (2005)CrossRefGoogle Scholar
  13. 13.
    Jakobi, N., Husbands, P., Harvey, I.: Noise and the reality gap: The use of simulation in evolutionary robotics. In: Morán, F., Merelo, J.J., Moreno, A., Chacon, P. (eds.) ECAL 1995. LNCS, vol. 929, pp. 704–720. Springer, Heidelberg (1995)Google Scholar
  14. 14.
    Kun, A.L., Miller, W.T.: Control of variable speed gaits for a biped robot. IEEE Robotics and Automation Magazine 6(3), 19–29 (1999)CrossRefGoogle Scholar
  15. 15.
    Liu, H., Iba, H.: A hierarchical approach for adaptive humanoid robot control. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Portland, Oregon, pp. 1546–1553, 20–23. IEEE Press, Los Alamitos (2004)Google Scholar
  16. 16.
    Miller III, W.T., Glanz, F.H., Kraft, L.G.: Application of a General Learning Algorithm to the Control of Robotic Manipulators. International Journal of Robotics Research 6(2), 84–98 (1987)CrossRefGoogle Scholar
  17. 17.
    Miller, W.T.: Real-Time Neural Network Control of a Biped Walking Robot. IEEE Control Systems Magazine, 41–48 (1994)Google Scholar
  18. 18.
    Pettersson, J., Sandholt, H., Wahde, M.: A flexible evolutionary method for the generation and implementation of behaviors for humanoid robots. In: Proceedings of the IEEE-RAS International Conference on Humanoid Robotic, Japan, November 22-24, pp. 279–286 (2001)Google Scholar
  19. 19.
    Shan, J., Junshi, C., Jiapin, C.: Design of central pattern generator for humanoid robot walking based on multi-objective GA. In: Proc. International Conference on Intelligent Robots and Systems (IROS 2000), vol. 3, pp. 1930–1935. IEEE-RSJ, Takamatsu (2000)Google Scholar
  20. 20.
    Sims, K.: Evolving 3D morphology and behavior by competition. Artificial Life 1(4), 353–372 (1994)CrossRefGoogle Scholar
  21. 21.
    Stanley, K.O., Miikkulainen, R.: A taxonomy for artificial embryogeny. Artificial Life 9(2), 93–130 (2003)CrossRefGoogle Scholar
  22. 22.
    Taga, G., Yamaguchi, Y., Shimizu, H.: Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biological Cybernetics 65, 147–159 (1991)zbMATHCrossRefGoogle Scholar
  23. 23.
    Takanishi, A., Ishid, M., Yamazaki, Y., Kato, I.: The realization of dynamic walking by the biped walking robot WL-10RD. In: Proceedings of the International Conference on Advanced Robotics (ICAR 1985), pp. 459–466 (1985)Google Scholar
  24. 24.
    Téllez, R.A., Angulo, C., Pardo, D.E.: Evolving the walking behaviour of a 12 DOF quadruped using a distributed neural architecture. In: Ijspeert, A.J., Masuzawa, T., Kusumoto, S. (eds.) BioADIT 2006. LNCS, vol. 3853, pp. 5–19. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Valsalam, V.K., Miikkulainen, R.: Modular neuroevolution for multi-legged locomotion. In: GECCO 2008: Proceedings of the 10th annual conference on Genetic and Evolutionary Computation, pp. 265–272. ACM, New York (2008)CrossRefGoogle Scholar
  26. 26.
    Wang, H., Lee, T.T., Gruver, W.A.: A neuromorphic controller for a three-link biped robot. IEEE Transactions on Systems, Man and Cybernetics 22(1), 164–169 (1992)CrossRefGoogle Scholar
  27. 27.
    Wolff, K., Nordin, P.: Learning biped locomotion from first principles on a simulated humanoid robot using linear genetic programming. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 495–506. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  28. 28.
    Ziegler, J., Barnholt, J., Busch, J., Banzhaf, W.: Automatic evolution of control programs for a small humanoid walking robot. In: Bidaud, P. (ed.) Proc. 5th International Conference on Climbing and Walking Robots (CLAWAR 2002), pp. 109–116. Professional Engineering Publishing (2002)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Departement of computer scienceLESIA Laboratory/Med Khider Biskra UniversityBiskraAlgeria
  2. 2.Institut de Recherche en Informatique de ToulouseUniversité de Toulouse - CNRS - UMR 5505ToulouseFrance

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