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Trends in Evolutionary Robotics

  • Lisa Meeden
  • Deepak Kumar
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 21)

Abstract

A review is given on the use of evolutionary techniques for the automatic design of adaptive robots. The focus is on methods which use neural networks and have been tested on actual physical robots. The chapter also examines the role of simulation and the use of domain knowledge in the evolutionary process. It concludes with some predictions about future directions in robotics.

Keywords

Mobile Robot Adaptive Behavior Evolutionary Computation Soft Computing Battery Level 
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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References

  1. [1]
    Van de Velde, W. (1991), “Toward Learning Robots,” Robotics and Autonomous Systems, Vol. 8, Nos. 1–2, pp. 1–6.Google Scholar
  2. [2]
    Smithers, T. (1993), “On Why Better Robots Make it Harder,” From Animals to Animats: Proceedings of the Second International Conference on Simulation of Adaptive Behavior, eds. Meyer, J-A., Roitblat, H., and Wilson, S., MIT Press, Cambridge, MA, pp. 64–72.Google Scholar
  3. [3]
    Cliff, D., Harvey, I., and Husbands, P. (1993), “Explorations in Evolutionary Robotics,” Adaptive Behavior, Vol. 2, No. 1, pp. 73–110.Google Scholar
  4. [4]
    Reynolds, C. (1994), “Evolution of Corridor Following Behavior in a Noisy World,” in From Animals to Animats: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, eds. Cliff, D., Husbands, P., Meyer, J-A., and Wilson, S., MIT Press, Cambridge, MA, pp. 402–410.Google Scholar
  5. [5]
    Nordin, P. and Banzhaf, W. (1997), “An On-Line Method to Evolve Behavior and to Control a Miniature Robot in Real Time with Genetic Programming,” Adaptive Behavior, Vol. 5, No. 2, pp. 107–140.Google Scholar
  6. [6]
    Ram, A., Boone, G., Arkin, R., and Pearce, M. (1994), “Using Genetic Algorithms to Learn Reactive Control Parameters for Autonomous Robotic Navigation,” Adaptive Behavior, Vol. 2, No. 3, pp. 277–305.Google Scholar
  7. [7]
    Colombetti, M., Dorigo, M. (1997), “Behavior Analysis and Training-A Methodology for Behavior Engineering,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 26, No. 3, pp. 365–380.Google Scholar
  8. [8]
    Grefenstette, J. (1992), “The Evolution of Strategies for Multi-Agent Environments,” Adaptive Behavior, Vol. 1, No. 1, pp. 65–90.Google Scholar
  9. [9]
    Nolfi, S., Floreano, D., Miglino, O., and Mondada, F. (1994), “How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics,” Artificial Live IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, eds. Brooks, R. and Maes, P., MIT Press, Cambridge, MA, pp. 190–197.Google Scholar
  10. [10]
    Back, T., Hammel, U., and Schwefel, H. (1997), “Evolutionary Computation: Comments on the History and Current State,” IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp. 3–17.Google Scholar
  11. [11]
    Holland, J. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.Google Scholar
  12. [12]
    Mitchell, M. (1996), An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA.Google Scholar
  13. [13]
    Goldberg, D. (1989), Genetic Algorithms in Search Optimization, and Machine Learning, Addison-Wesley Publishing Company, New York.Google Scholar
  14. [14]
    Koza, J. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA.Google Scholar
  15. [15]
    Rumelhart, D., Hinton, G., and Williams, R. (1986), “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing, Vol. 1, eds. McClelland, J. and Rumelhart, D., MIT Press, Cambridge, MA, pp. 318362.Google Scholar
  16. [16]
    Elman, J. (1990), “Finding Structure in Time,” Cognitive Science, Vol. 14, pp. 179–212.CrossRefGoogle Scholar
  17. [17]
    Mondada, R. Franzi, E., and Ienne, P. (1993), “Mobile Robot Miniaturization: A Tool for Investigation in Control Algorithms,” Proceedings of the Third International Symposium on Experimental Robots, Kytoto, Japan.Google Scholar
  18. [18]
    Pomerleau, D. (1993), Neural Network Perception for Mobile Robot Guidance, Kluwer, Norwell, MA.Google Scholar
  19. [19]
    Jochem, T. and Pomerleau, D. (1996), “Life in the Fast Lane: The Evolution of an Adaptive Vehicle Control System,” AI Magazine, Vol. 17, No. 2, pp. 11–50.Google Scholar
  20. [20]
    Floreano, D. and Mondada, F. (1996), “Evolution of Homing Navigation in a Real Mobile Robot,” IEEE Transactions of Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 26, No. 3., pp. 396–407.Google Scholar
  21. [21]
    Nolfi, S. (1997), “Using Emergent Modularity to Develop Control Systems for Mobile Robots,” Adaptive Behavior, Vol. 5, No. 3/4, pp. 343–363.CrossRefGoogle Scholar
  22. [22]
    Harvey, I., Husbands, P., and Cliff, D. (1994), “Seeing the Light: Artificial Evolution, Real Vision,” From Animals to Animats: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, eds. Cliff, D., Husbands, P., Meyer, J-A., and Wilson, S., MIT Press, Cambridge, MA, pp. 392–401.Google Scholar
  23. [23]
    Harvey, I. (1993), “Evolutionary Robotics and SAGA: The Case for Hill Crawling and Tournament Selection,” Artificial Life III, ed. Langton, C., Addison Wesley, Reading, MA, pp. 299–326.Google Scholar
  24. [24]
    Gomez, F. and Miikkulainen, R. (1997), “Incremental Evolution of Complex General Behavior,” Adaptive Behavior, Vol. 5, No. 3/4, pp. 317–342.CrossRefGoogle Scholar
  25. [25]
    Meeden, L. (1996), “An Incremental Approach to Developing Intelligent Neural Network Controllers for Robots,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 26, No. 3, pp. 474–485.CrossRefGoogle Scholar
  26. [26]
    Baluja, S. (1996), “Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 26, No. 3, pp. 450–463.CrossRefGoogle Scholar
  27. [27]
    Floreano, D. and Mondada, F. (1994), “Automatic Creation of an Autonomous Agent: Genetic Evolution of a Neural-Network Driven Robot,” From Animals to Animats: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, eds. Cliff, D., Husbands, P., Meyer, J-A., and Wilson, S., MIT Press, Cambridge, MA, pp. 421–430.Google Scholar
  28. [28]
    Brady, M. and Hu, H. (1997), “Software and Hardware Architectures of Advanced Mobile Robots for Manufacturing,” Journal of Experimental and Theoretical Artificial Intelligence, Vol. 9, pp. 257–276.CrossRefGoogle Scholar
  29. [29]
    Jakobi, N., Husbands, P. and Harvey, I. (1995), “Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics,” Advances in Artificial Life: Proceedings of the Third European Conference on Artificial Life, Granada, Spain, June4–6, 1995, eds. Moran, F., Moreno, A., Merelo, J., and Chacon, P., Springer-Verlag, Lecture Notes in Artificial Intelligence 929, pp. 704–720.Google Scholar
  30. [30]
    Husbands, P., Harvey, I., Cliff, D., and Miller, G. (1997), “Artificial Evolution: A New Path for Artificial Intelligence?,” Brain and Cognition, Vol. 34, pp. 130159.Google Scholar
  31. [31]
    Hexmoor, H., Kortenkamp, D., and Horswill, I. (1997), “Software Architectures for Hardware Agents,” Journal of Experimental and Theoretical Artificial Intelligence, Vol. 9, pp. 147–156.CrossRefGoogle Scholar
  32. [32]
    Harvey, I., Husbands, P., and Cliff, D. (1993), “Issues in Evolutionary Robotics,” From Animals to Animats: Proceedings of the Second International Conference on Simulation of Adaptive Behavior, eds. Meyer, J-A., Roitblat, H. and Wilson, S., MIT Press, Cambridge, MA, pp. 364–373.Google Scholar
  33. [33]
    Meeden, L., McGraw, G., and Blank, D. (1993), “Emergent Control and Planning in an Autonomous Vehicle,” Proceedings of the Fifteenth Annual Meeting of the Cognitive Science Society, Lawrence Earlbaum Associates, Hillsdale, NJ, pp. 735–740Google Scholar
  34. [34]
    Dorigo, M. and Colombetti, M. (1997), “Precis of Robot Shaping: An Experiment in Behavior Engineering,” Adaptive Behavior, Vol. 5, No. 3/4, pp. 391–405.CrossRefGoogle Scholar
  35. [35]
    Hexmoor, H. and Meeden, L. (1997), “Learning in Autonomous Robots: A Summary of the 1996 Robolearn Workshop,” Knowledge Engineering Review, Vol. 11, No. 1.Google Scholar
  36. [36]
    Grefenstette, J., and Schultz, A. (1994), “An Evolutionary Approach to Learning in Robots,” Naval Research Laboratory Technical Report AIC-94–014.Google Scholar
  37. [37]
    Grefenstette, J., Ramsey, C., and Schultz, A. (1990), “Learning Sequential Decision Rules Using Simulation Models and Competition,” Machine Learning, Vol. 5, No. 4, pp. 355–381.Google Scholar
  38. [38]
    Clark, A. (1993), Associative Engines: Connectionism, Concepts, and Representational Change, MIT Press, Cambridge, MA.Google Scholar
  39. [39]
    Moravec, H. (1992), “The Universal Robot,” Visions of the Future: Art, Technology and Computing in the Twenty-First Century, ed. Pickover, C., St. Martin’s Press, New York, pp. 65–73.Google Scholar
  40. [40]
    Brooks, R. and Stein, L. (1994), “Building Brains for Bodies,” Autonomous Robots, Vol. 1, No. 1, pp. 7–25.CrossRefGoogle Scholar
  41. [41]
    Ferrell, C. (1996), “Orientation Behavior Using Registered Topographic Maps,” From Animals to Animats: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, MIT Press, Cambridge, MA.Google Scholar
  42. [42]
    Marjanovic, M. Scassellati, B., and Williamson, M. (1996), “Self-Taught Visually Guided Pointing for a Humanoid Robot,” From Animals to Animats: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, MIT Press, Cambridge, MA.Google Scholar
  43. [43]
    Meeden, L. (1996), “Using Robotics as an Introduction to Computer Science,” Proceedings of the Ninth Florida Artificial Intelligence Research Symposium, ed. Stewman, J., pp. 473–477.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Lisa Meeden
    • 1
  • Deepak Kumar
    • 2
  1. 1.Computer ScienceSwarthmore CollegeSwarthmoreUSA
  2. 2.Department of MathematicsBryn Mawr CollegeBryn MawrUSA

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