Evolving neural controllers for temporally dependent behaviors in autonomous robots

  • J. Santos
  • R. J. Duro
2. Modification Tasks Perceptual Robotics
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1416)


The objective of this work is to study neural control architectures for autonomous robots that explicitly handle time in tasks that require reasoning with the temporal component. The controllers are generated and trained through the methodology of evolutionary robotics. In this study, the reasoning processes are circumscribed to data provided by light sensors, as a first step in the process of evaluating the requirements of control structures that can be extended to the processing of visual information provided by cameras.


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  1. 1.
    Elman, J.L., and Zipser, D., Learning the Hidden Structure of Speech, Techn. Report8701, Institute for Cognitive Ssience, University of California, San Diego, 1987.Google Scholar
  2. 2.
    Jordan, M.I., Attractor Dynamics and Parallelism in a Connectionist Sequential Machine, In Proceedings of the 1986 Cognitive Science Conference, Erlbaum, L., and Hillsdale, N.J. (Eds), pp. 531–546, 1986.Google Scholar
  3. 3.
    Day, S.P., and Davenport, M.R., Continuous Time Temporal Backpropagation with Adaptable Time Delays, IEEE Transactions on Neural Networks, Vol. 4, No. 2, pp. 348–354, 1993.Google Scholar
  4. 4.
    Duro, R.J., and Santos, J., Fast Discrete Time Backpropagation for Adaptive Synaptic Delay Based Neural Networks, Submitted for publication in IEEE Transactions on Neural Networks, 1997.Google Scholar
  5. 5.
    Waibel, A., Hanazawa, T., Hinton, G., Lang, J., and Shikano, K., Phoneme Recognition Using Time Delay Neural Networks, IEEE Trans. Acoust. Speech Signal Processing 37, pp. 328–339, 1989.Google Scholar
  6. 6.
    Holland, J. H., Adaptation in Natural and Artificial Systems, Ann Argon University of Michigan Press, 1975.Google Scholar
  7. 7.
    Schwefel, H., Kybernetische Evolution als Strategie der Experimentellen Forschung in der Strmungstechnik, Diploma Thesis, Technical University, Berlin, 1965.Google Scholar
  8. 8.
    Cliff, D.T., Harvey, I., and Husbands, P., Explorations in Evolutionary Robotics, Adaptive Behavior, Vol. 2, pp. 73–110, 1993.Google Scholar
  9. 9.
    Nolfi, S., Floreano, D., Miglino, O., and Mondada, F., How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics, In R. Brooks and P. Maes (Eds.), Proceedings of Fourth International Conference on Artificial Life, Cambridge, MA, MIT Press, 1994.Google Scholar
  10. 10.
    Miglino, O., Lund, H.H., and Nolfi, S., Evolving Mobile Robots in Simulated an Real Enviroments, Artificial Life 2:4, pp. 417–434, 1996.Google Scholar
  11. 11.
    Beer, R., and Gallagher, J., Evolving Dynamical Neural Networks for Adaptive Behavior, Adaptive Behavior, Vol. 1, No. 1, pp. 91–122, 1992.Google Scholar
  12. 12.
    Kodjabachian, J., and Meyer, J.A., Evolution and Development of Modular Control Architectures for 1-D Locomotion in Six-Legged Animats, Surnitted for publication, 1997.Google Scholar
  13. 13.
    Cliff, D.T., Husbands, P., and Harvey, I., Evolving Visually Guided Robots, Proceedings of SAB92, Second International Conference on Simulation of Adaptive Behaviour, Meyer, J.A., Roitblat, H., and Wilson, S. (Eds.), Cambridge. MA, 1993.Google Scholar
  14. 14.
    Lund, H.H., and Hallam, J., Sufficient Neurocontrollers can be Surprisingly Simple, Research paper 824, Department of Artificial Intelligence, Univ. Edinburg, 1996.Google Scholar
  15. 15.
    Mondada, F., Franzi, E., and Ienne, P. Mobile Robot Miniaturisation: A Tool for Investigating in Control Algorithms, Experimental Robotics III, Lecture Notes in Control and Information Sciences, Vol. 200, pp. 501–513, Springer-Verlag, 1994.Google Scholar
  16. 16.
    Mitchel, O., Khepera Simulator Package version 2.0: Freeware mobile robot simulator, (Downloadable from om/khep-sim.html), University of Nice Sophia-Antipolis, France, 1996.Google Scholar
  17. 17.
    Floreano, D., and Mondada, F., Evolution of Homing Navigation in a Real Mobile Robot, In IEEE Transactions on Systems, Man and Cybernetics, Vol. 20, 1996.Google Scholar
  18. 18.
    Duro, R.J., Santos, J., and Sarmiento, A., GENIAL: An Evolutionary Recurrent Neural Network Designer and Trainer, In Computer Aided Systems Theory-CAST'94, Tuncer I. Oren & George J. Klir (Eds.), Lecture Notes in Computer Science, Vol. 1105, pp. 295–301, 1996.Google Scholar
  19. 19.
    Santos, J., and Duro, R.J., Evolutionary Design of ANN Architectures for the Detection of Patterns in Signals, FEA '97 (Frontiers in Evolutionary Algorithms)-Joint Conference of Information Sciences, Vol. I, pp. 100–103, North Caroline, March 1997.Google Scholar
  20. 20.
    Santos, J., and Duro, R.J., Evolutionary Generation and Training of Recurrent Artificial Neural Networks, Proceedings of The IEEE World Congress on Computational Intelligence, Vol. II, 759–763, Orlando, Florida, June-July 1994.Google Scholar
  21. 21.
    Floreano, D., and Nolfi, S., Adaptive Behavior in Competing Co-Evolving Species, In ECAL'97 (Fourth European Conference on Artificial Life), Phil Husbands and Irman Harvey (Eds.), Complex Adaptive Systems Series, MIT Press, 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • J. Santos
    • 1
  • R. J. Duro
    • 2
  1. 1.Departamento de ComputaciónUniversidade da CorunaLa CorunaSpain
  2. 2.Departamento de Ingeniería IndustrialUniversidade da CorufiaFerrol (La Coruña)Spain

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