Evolutionary Robotics: Exploring New Horizons

  • Conference paper
New Horizons in Evolutionary Robotics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 341))

Abstract

This paper considers the field of Evolutionary Robotics (ER) from the perspective of its potential users: roboticists. The core hypothesis motivating this field of research is discussed, as well as the potential use of ER in a robot design process. Four main aspects of ER are presented: (a) ER as an automatic parameter tuning procedure, which is the most mature application and is used to solve real robotics problem, (b) evolutionary-aided design, which may benefit the designer as an efficient tool to build robotic systems (c) ER for online adaptation, i.e. continuous adaptation to changing environment or robot features and (d) automatic synthesis, which corresponds to the automatic design of a mechatronic device and its control system. Critical issues are also presented as well as current trends and pespectives in ER. A section is devoted to a roboticist’s point of view and the last section discusses the current status of the field and makes some suggestions to increase its maturity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abbeel, P., Coates, A., Quigley, M., Ng, A.: An application of reinforcement learning to aerobatic helicopter flight. In: Advances in Neural Information Processing Systems (NIPS), vol. 19. MIT Press, Cambridge (2007)

    Google Scholar 

  2. Amil, M., Bredeche, N., Gagné, C., Gelly, S., Schoenauer, M., Teytaud, O.: A statistical learning perspective of genetic programming. In: Proceedings of the 12th European Conference on Genetic Programming at Evostar 2009 (2009)

    Google Scholar 

  3. Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M., Yoshida, C.: Cognitive developmental robotics: a survey. IEEE Transactions on Autonomous Mental Development 1(1), 12–34 (2009)

    Article  Google Scholar 

  4. Auerbach, J., Bongard, J.: How Robot Morphology and Training Order Affect the Learning of Multiple Behaviors. In: Proceedings of the IEEE Congress on Evolutionary Computation (2009)

    Google Scholar 

  5. Baele, G., Bredeche, N., Haasdijk, E., Maere, S., Michiels, N., van de Peer, Y., Schmickl, T., Schwarzer, C., Thenius, R.: Open-ended on-board evolutionary robotics for robot swarms. In: IEEE Congress on Evolutionary Computation, CEC 2009 (2009)

    Google Scholar 

  6. Bartz-Beielstein, T., Preuss, M.: Experimental research in evolutionary computation. In: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, pp. 3001–3020. ACM, New York (2007)

    Chapter  Google Scholar 

  7. Beyer, H.G.: The Theory of Evolution Strategies. Springer, Heidelberg (2001)

    Google Scholar 

  8. Beyer, H.G., Schwefel, H.P.: Evolution strategies – A comprehensive introduction. Natural Computing 1, 3–52 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  9. Birattari, M., Zlochin, M., Dorigo, M.: Towards a theory of practice in metaheuristics design: A machine learning perspective. RAIRO–Theoretical Informatics and Applications 40, 353–369 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  10. Blum, A.: On-line algorithms in machine learning. In: Proceedings of the Workshop on On-Line Algorithms, Dagstuhl, pp. 306–325. Springer, Heidelberg (1996)

    Google Scholar 

  11. Bongard, J., Lipson, H.: Nonlinear system identification using coevolution of models and tests. IEEE Transactions on Evolutionary Computation 9(4), 361–384 (2005)

    Article  Google Scholar 

  12. Bongard, J., Lipson, H.: Automated reverse engineering of nonlinear dynamical systems. Proceedings of the National Academy of Sciences 104(24), 9943–9948 (2007)

    Article  MATH  Google Scholar 

  13. Bongard, J., Zykov, V., Lipson, H.: Resilient machines through continuous self-modeling. Science 314(5802), 1118–1121 (2006)

    Article  Google Scholar 

  14. Bredeche, N., Haasdijk, E., Eiben, A.: On-line, On-board Evolution of Robot Controllers. In: Evolution Artificielle / Artificial Evolution. Strasbourg France (2009)

    Google Scholar 

  15. Bredeche, N., Montanier, J.-M.: Environment-driven Embodied Evolution in a Population of Autonomous Agents. In: Schaefer, R., et al. (eds.) PPSN XI. LNCS, vol. 6239, pp. 290–299. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  16. D’Ambrosio, D.B., Stanley, K.O.: A novel generative encoding for exploiting neural network sensor and output geometry. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007 (2007)

    Google Scholar 

  17. Darwin, C.: On the Origin of Species by Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. John Murray, London (1859)

    Google Scholar 

  18. Deb, K.: Multi-objectives optimization using evolutionnary algorithms. Wiley, Chichester (2001)

    Google Scholar 

  19. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  20. Deb, K., Srinivasan, A.: Innovization: Innovating design principles through optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1629–1636. ACM, New York (2006)

    Chapter  Google Scholar 

  21. Deb, K., Srinivasan, A.: INNOVIZATION: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization. In: Multiobjective Problem Solving from Nature: From Concepts to Applications, pp. 243–262 (2007)

    Google Scholar 

  22. Doncieux, S., Hamdaoui, M.: Evolutionary Algorithms to Analyse and Design a Controller for a Flap** Wings Aircraft. In: New Horizons in Evolutionary Robotics: Post-Proceedings of the 2009 EvoDeRob Workshop. Springer, Heidelberg (2010)

    Google Scholar 

  23. Doncieux, S., Mouret, J.B.: Behavioral diversity measures for evolutionary robotics. In: IEEE Congress on Evolutionary Computation, CEC 2010 (to appear, 2010)

    Google Scholar 

  24. Eiben, A., Haasdijk, E., Bredeche, N.: Embodied, on-line, on-board evolution for autonomous robotics. In: Levi, P., Kernbach, S. (eds.) Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution, Cognitive Systems Monographs, vol. 7, pp. 361–382. Springer, Heidelberg (2010)

    Google Scholar 

  25. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  26. Fisher, R.: Design of Experiments. British Medical Journal 1(3923), 554 (1936)

    Article  Google Scholar 

  27. Fleming, P.J., Purshouse, R.C.: Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice 10(11), 1223–1241 (2002)

    Article  Google Scholar 

  28. Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. In: Intelligent Robotics and Autonomous Agents. MIT Press, Cambridge (2008)

    Google Scholar 

  29. Floreano, D., Mondada, F.: Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics-Part B (1996)

    Google Scholar 

  30. Floreano, D., Mondada, F.: Evolutionary neurocontrollers for autonomous mobile robots. Neural Networks 11, 1461–1478 (1998)

    Article  Google Scholar 

  31. García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC 2005 special session on real parameter optimization. Journal of Heuristics 15(6), 617–644 (2009)

    Article  MATH  Google Scholar 

  32. Gauci, J.J., Stanley, K.O.: Generating large-scale neural networks through discovering geometric regularities. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007 (2007)

    Google Scholar 

  33. Gloye, A., Wiesel, F., Tenchio, O., Simon, M.: Reinforcing the driving quality of soccer playing robots by anticipation (verbesserung der fahreigenschaften von fu ballspielenden robotern durch antizipation). IT - Information Technology 47, 250–257 (2005)

    Article  Google Scholar 

  34. Godzik, N., Schoenauer, M., Sebag, M.: Evolving symbolic controllers. In: Evo Workshops, pp. 638–650 (2003)

    Google Scholar 

  35. Goldberg, D.: Genetic Algorithms in Search and Optimization. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  36. Gomez, F., Miikkulainen, R.: Incremental evolution of complex general behavior. Adaptive Behavior 5(3-4), 317–342 (1997)

    Article  Google Scholar 

  37. Greenwood, G.W., Tyrrell, A.M.: Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems. Wiley-IEEE Press (2006)

    Google Scholar 

  38. Gross, R., Bonani, M., Mondada, F., Dorigo, M.: Autonomous self-assembly in swarm-bots. IEEE Transactions on Robotics 22(6), 1115–1130 (2006)

    Article  Google Scholar 

  39. Gruau, F.: Neural Network Synthesis Using Cellular Encoding and the Genetic Algorithm. Ph.D. thesis, Claude Bernard-Lyon I University (1994)

    Google Scholar 

  40. Gruau, F.: Automatic definition of modular neural networks. Adaptive Behaviour 3(2), 151–183 (1995)

    Article  Google Scholar 

  41. Hamda, H., Jouve, F., Lutton, E., Schoenauer, M., Sebag, M.: Compact unstructured representations in evolutionary topological optimum design. Applied Intelligence 16, 139–155 (2002)

    Article  MATH  Google Scholar 

  42. Hamda, H., Schoenauer, M.: Adaptive techniques for evolutionary topological optimum design. In: Parmee, I. (ed.) Evolutionary Design and Manufacture, pp. 123–136. Springer, Heidelberg (2000)

    Google Scholar 

  43. Hansen, N., Muller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation 11(1), 1–18 (2003)

    Article  Google Scholar 

  44. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary computation 9(2), 159–195 (2001)

    Article  Google Scholar 

  45. Hara, F., Pfeifer, R.: Morpho-Functional Machines: The New Species: Designing Embodied Intelligence. Springer, Heidelberg (2003)

    Google Scholar 

  46. Hartland, C., Bredeche, N., Sebag, M.: Memory-enhanced evolutionary robotics. In: IEEE Congress on Evolutionary Computation (2009)

    Google Scholar 

  47. Hauert, S., Zufferey, J.C., Floreano, D.: Reverse-engineering of Artificially Evolved Controllers for Swarms of Robots. In: IEEE Congress on Evolutionary Computation (2009)

    Google Scholar 

  48. Holland, J.H.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  49. Hornby, G.S.: Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design. In: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 1729–1736 (2005)

    Google Scholar 

  50. Hornby, G.S., Takamura, S., Yokono, J., Hanagata, O., Yamamoto, T., Fujita, M.: Evolving robust gaits with aibo. In: IEEE International Conference on Robotics and Automation, pp. 3040–3045 (2000)

    Google Scholar 

  51. Jakobi, N.: Evolutionary robotics and the radical envelope-of-noise hypothesis. Adaptive Behavior 6(2), 325–368 (1997)

    Article  Google Scholar 

  52. Khalil, W., Dombre, E.: Modeling, Identification and Control of Robots, 3rd edn. Taylor & Francis, Inc., Abington (2002)

    Google Scholar 

  53. Kicinger, R., Arciszewski, T., Jong, K.: Evolutionary computation and structural design: A survey of the state-of-the-art. Computers & Structures 83(23-24), 1943–1978 (2005)

    Article  Google Scholar 

  54. Kim, K.J., Cho, S.B.: Robot Action Selection for Higher Behaviors with CAM-Brain Modules. In: Proceedings of the 32nd ISR (International Symposium on Robotics), vol. 19, p. 21 (2001)

    Google Scholar 

  55. Kodjabachian, J., Meyer, J.A.: Evolution and development of neural networks controlling locomotion, gradient-following, and obstacle-avoidance in artificial insects. IEEE Transactions on Neural Networks 9, 796–812 (1997)

    Article  Google Scholar 

  56. Koos, S., Mouret, J.B., Doncieux, S.: Automatic system identification based on coevolution of models and tests. In: IEEE Congress on Evolutionary Computation, CEC 2009 (2009)

    Google Scholar 

  57. Koos, S., Mouret, J.B., Doncieux, S.: Crossing the reality gap in evolutionary robotics by promoting transferable controllers. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, ACM, New York (2010)

    Google Scholar 

  58. Kramer, O., Gloger, B., Goebels, A.: An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 674–681. ACM, New York (2007)

    Chapter  Google Scholar 

  59. Kwok, D.P., Sheng, F.: Genetic algorithm and simulated annealing for optimal robot arm PID control. In: Proceedings of the First IEEE Conference on IEEE World Congress on Computational Intelligence, pp. 707–713 (1994)

    Google Scholar 

  60. Lehman, J., Stanley, K.O.: Exploiting Open-Endedness to Solve Problems Through the Search for Novelty. Artificial Life 11, 329 (2008)

    Google Scholar 

  61. Lehman, J., Stanley, K.O.: Abandoning objectives: Evolution through the search of novelty alone. Evolutionary Computation (2010)

    Google Scholar 

  62. Lipson, H.: Principles of Modularity, Regularity, and Hierarchy for Scalable Systems. In: Genetic and Evolutionary Computation Conference (GECCO 2004) Workshop on Modularity, regularity and Hierarchy (2004)

    Google Scholar 

  63. Lipson, H., Bongard, J., Zykov, V., Malone, E.: Evolutionary robotics for legged machines: from simulation to physical reality. Intelligent Autonomous Systems 9, 9 (2006)

    Google Scholar 

  64. Lipson, H., Pollack, J.B.: Automatic design and manufacture of robotic life forms. Nature 406(406), 974–978 (2000)

    Google Scholar 

  65. Lohn, J., Crawford, J., Globus, A., Hornby, G.S., Kraus, W., Larchev, G., Pryor, A., Srivastava, D.: Evolvable systems for space applications. In: International Conference on Space Mission Challenges for Information Technology (2003)

    Google Scholar 

  66. Lohn, J., Hornby, G., Linden, D.: An evolved antenna for deployment on NASAs space technology 5 mission. In: Genetic Programming Theory and Practice II, pp. 301–315 (2004)

    Google Scholar 

  67. Lohn, J.D., Linden, D.S., Hornby, G.S., Kraus, W.F., Rodriguez-Arroyo, A.: Evolutionary design of an x-band antenna for nasa’s space technology 5 mission. In: Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware, EH 2003, IEEE Computer Society Press, Washington (2003)

    Google Scholar 

  68. Manos, S., Large, M.C.J., Poladian, L.: Evolutionary design of single-mode microstructured polymer optical fibres using an artificial embryogeny representation. In: GECCO 2007: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, pp. 2549–2556. ACM, New York (2007)

    Chapter  Google Scholar 

  69. Metta, G., Sandini, G., Vernon, D., Natale, L., Nori, F.: The iCub humanoid robot: an open platform for research in embodied cognition. In: Permis: Performance Metrics for Intelligent Systems Workshop. Washington DC, USA (2008)

    Google Scholar 

  70. Meyer, J.A., Guillot, A.: Biologically-inspired Robots. In: Handbook of Robotics. Springer, Heidelberg (2008)

    Google Scholar 

  71. Montanier, J.M., Bredeche, N.: Embedded evolutionary robotics: The (1+1)-restart-online adaptation algorithm. In: Proceedings of IROS Workshop Exploring New Horizons in the Evolutionary Design of Robots (2009)

    Google Scholar 

  72. Mouret, J.B.: Novelty-based multiobjectivization. In: Proceedings of IROS Workshop Exploring New Horizons in the Evolutionary Design of Robots (2009)

    Google Scholar 

  73. Mouret, J.B., Doncieux, S.: Incremental evolution of animats’ behaviors as a multi-objective optimization. In: Asada, M., Hallam, J.C.T., Meyer, J.-A., Tani, J. (eds.) SAB 2008. LNCS (LNAI), vol. 5040, pp. 210–219. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  74. Mouret, J.B., Doncieux, S.: MENNAG: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars. Evolutionary Intelligence 1(3), 187–207 (2008)

    Article  Google Scholar 

  75. Mouret, J.B., Doncieux, S.: Evolving modular neural-networks through exaptation. In: IEEE Congress on Evolutionary Computation, CEC 2009 (2009)

    Google Scholar 

  76. Mouret, J.B., Doncieux, S.: Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. In: IEEE Congress on Evolutionary Computation, CEC 2009 (2009)

    Google Scholar 

  77. Mouret, J.B., Doncieux, S.: Using behavioral exploration objectives to solve deceptive problems in neuro-evolution. In: GECCO 2009: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation. ACM, New York (2009)

    Google Scholar 

  78. Mouret, J.B., Doncieux, S., Meyer, J.A.: Incremental evolution of target-following neuro-controllers for flap**-wing animats. In: Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J.C.T., Marocco, D., Meyer, J.-A., Miglino, O., Parisi, D. (eds.) SAB 2006. LNCS (LNAI), vol. 4095, pp. 606–618. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  79. Nolfi, S., Floreano, D.: How co-evolution can enhance the adaptive power of artificial evolution: Implications for evolutionary robotics. In: Proceedings of the First European Workshop on Evolutionary Robotics (EvoRobot 1998), pp. 22–38 (1998)

    Google Scholar 

  80. Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2001)

    Google Scholar 

  81. Oudeyer, P.Y., Kaplan, F., Hafner, V.: Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation 1(11), 265–286 (2007)

    Article  Google Scholar 

  82. Pollack, J.B., Lipson, H.: The golem project: Evolving hardware bodies and brains. In: EH 2000: Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware, p. 37. IEEE Computer Society, Los Alamitos (2000)

    Chapter  Google Scholar 

  83. Preble, S., Lipson, H., Lipson, M.: Two-dimensional photonic crystals designed by evolutionary algorithms. Applied Physics Letters 86 (2005)

    Google Scholar 

  84. Radcliffe, N.: The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence 10(4), 339–384 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  85. Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart (1973)

    Google Scholar 

  86. Ronald, S.: Robust encodings in genetic algorithms: a survey of encoding issues. In: IEEE International Conference on Evolutionary Computation, pp. 43–48 (1997)

    Google Scholar 

  87. Rothlauf, F.: Representations for Genetic And Evolutionary Algorithms. Springer, GmbH & Co. K, Heidelberg (2006)

    Google Scholar 

  88. Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. Science 324(5923), 81–85 (2009)

    Article  Google Scholar 

  89. Schwefel, H.P.: Numerical Optimization of Computer Models. John Wiley & Sons, Inc., New York (1981)

    MATH  Google Scholar 

  90. Shim, Y., Husbands, P.: Feathered Flyer: Integrating Morphological Computation and Sensory Reflexes into a Physically Simulated Flap**-Wing Robot for Robust Flight Manoeuvre. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds.) ECAL 2007. LNCS (LNAI), vol. 4648, pp. 756–765. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  91. Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: Modelling, Planning and Control. Springer, Heidelberg (2008)

    Google Scholar 

  92. Sigaud, O., Peters, J. (eds.): From Motor Learning to Interaction Learning in Robots. Studies in Computational Intelligence, vol. 264, pp. 1–12. Springer, Heidelberg (2010)

    Book  MATH  Google Scholar 

  93. Sims, K.: Evolving virtual creatures. In: SIGGRAPH 1994: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 15–22. ACM, New York (1994)

    Chapter  Google Scholar 

  94. Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10(2), 99–127 (2002)

    Article  Google Scholar 

  95. Stanley, K.O., Miikkulainen, R.: A taxonomy for artificial embryogeny. Artificial Life 9(2), 93–130 (2003)

    Article  Google Scholar 

  96. Stanley, K.O., Miikkulainen, R.: Competitive Coevolution through Evolutionary Complexification. Journal of Artificial Intelligence Research 21, 63–100 (2004)

    Google Scholar 

  97. Usui, Y., Arita, T.: Situated and embodied evolution in collective evolutionary robotics. In: Proc. of the 8th International Symposium on Artificial Life and Robotics, pp. 212–215 (2003)

    Google Scholar 

  98. Vanderborght, B., Verrelest, B., Van Ham, R., Van Damme, M., Beyl, P., Lefeber, D.: Development of a compliance controller to reduce energy consumption for bipedal robots. Autonomous Robots 24(4), 419–434 (2008)

    Article  Google Scholar 

  99. Wahde, M.: A method for behavioural organization for autonomous robots based on evolutionary optimization of utility functions. Proceedings of the I MECH E Part I Journal of Systems & Control Engineering 217(4), 249–258 (2003)

    Google Scholar 

  100. Watson, R.A., Ficici, S.G., Pollack, J.B.: Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In: 1999 Congress on Evolutionary Computation, pp. 335–342 (1999)

    Google Scholar 

  101. Watson, R.A., Ficici, S.G., Pollack, J.B.: Embodied evolution: Distributing an evolutionary algorithm in a population of robots. Robotics and Autonomous Systems 39(1), 1–18 (2002)

    Article  Google Scholar 

  102. Wolff, K., Sandberg, D., Wahde, M.: Evolutionary optimization of a bipedal gait in a physical robot. In: IEEE Congress on Evolutionary Computation, CEC 2008, pp. 440–445 (2008)

    Google Scholar 

  103. Zinn, M., Khatib, O., Roth, B., Salisbury, J.: Playing it safe [human-friendly robots]. IEEE Robotics Automation Magazine 11(2), 12–21 (2004), doi:10.1109/MRA.2004.1310938

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Doncieux, S., Mouret, JB., Bredeche, N., Padois, V. (2011). Evolutionary Robotics: Exploring New Horizons. In: Doncieux, S., Bredèche, N., Mouret, JB. (eds) New Horizons in Evolutionary Robotics. Studies in Computational Intelligence, vol 341. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18272-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-18272-3_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18271-6

  • Online ISBN: 978-3-642-18272-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Navigation