EvoSphere: The World of Robot Evolution

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9477)

Abstract

In this paper I describe EvoSphere, a tangible realization of the general Evolution of Things concept. EvoSphere can be used as a research platform to study the evolution of intelligent machines for practical as well as theoretical purposes. On the one hand, it can be used to develop robots that are hard to obtain with traditional design and optimization techniques and it can deliver original solutions that are unlikely to be conceived by a human designer. On the other hand, EvoSphere forms an evolving ecosystem that enables fundamental research into evolution and embodied intelligence. The use of real hardware is a pivotal feature as it avoids the reality gap and guarantees that the evolved solutions are physically feasible. On the long term, EvoSphere technology can pave the way for robot populations that evolve ‘in the wild’ and can adapt to unforeseen and changing circumstances.

Keywords

Evolutionary robotics Embodied evolution Artificial life Evolution of things 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Auerbach, J.E., Aydin, D., Maesani, A., Kornatowski, P., Cieslewski, T., Heitz, G., Fernando, P.R., Loshchilov, I., Daler, L., Floreano, D.: Robogen: robot generation through artificial evolution. In: Sayama, H., Rieffel, J., Risi, S., Doursat, R., Lipson, H. (eds.) Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, pp. 136–137. The MIT Press (2014)Google Scholar
  2. 2.
    Auerbach, J.E., Bongard, J.C.: Environmental influence on the evolution of morphological complexity in machines. PLOS Comput. Biol. 10(1), e1003399 (2014)CrossRefGoogle Scholar
  3. 3.
    Bentley, P., Corne, D.: Creative Evolutionary Systems. Morgan Kaufmann, San Francisco (2002)Google Scholar
  4. 4.
    Bentley, P.J. (ed.): Evolutionary Design by Computers. Morgan Kaufmann, San Francisco (1999)MATHGoogle Scholar
  5. 5.
    Bongard, J.C., Lipson, H.: Evolved machines shed light on robustness and resilience. Proc. IEEE 302(5), 899–914 (2014)CrossRefGoogle Scholar
  6. 6.
    Bongard, J.C., Pfeifer, R.: Evolving complete agents using artificial ontogeny. In: Hara, F., Pfeifer, R. (eds.) Morpho-Functional Machines: The New Species, pp. 237–258. Springer, Tokyo (2003)CrossRefGoogle Scholar
  7. 7.
    Brodbeck, L., Hauser, S., Iida, F.: Morphological evolution of physical robots through model-free phenotype development. PLoS One 10(6), e0128444 (2015)CrossRefGoogle Scholar
  8. 8.
    Eiben, A.E.: In Vivo Veritas: towards the evolution of things. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 24–39. Springer, Heidelberg (2014) Google Scholar
  9. 9.
    Eiben, A.E., Kernbach, S., Haasdijk, E.: Embodied artificial evolution - artificial evolutionary systems in the 21st century. Evol. Intell. 5(4), 261–272 (2012)CrossRefGoogle Scholar
  10. 10.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series, 2nd edn. Springer, Heidelberg (2015) CrossRefMATHGoogle Scholar
  11. 11.
    Eiben, A.E., Bredeche, N., Hoogendoorn, M., Stradner, J., Timmis, J., Tyrrell, A.M., Winfield, A.: The triangle of life: evolving robots in real-time and real-space. In: Liò, P., Miglino, O., Nicosia, G., Nolfi, S., Pavone, M. (eds.) Advances In Artificial Life, ECAL 2013, pp. 1056–1063. MIT Press, (2013)Google Scholar
  12. 12.
    Eiben, A.E., Smith, J.: From evolutionary computation to the evolution of things. Nature 521(7553), 476–482 (2015)CrossRefGoogle Scholar
  13. 13.
    Floreano, D., Keller, L.: Evolution of adaptive behaviour in robots by means of darwinian selection. PLOS Biol. 8(1), e1000292 (2010)CrossRefGoogle Scholar
  14. 14.
    Jakobi, N., Husbands, P., Harvey, I.: Noise and the reality gap: the use of simulation in evolutionary robotics. In: Morán, F., Moreno, A., Merelo, J.J., Chacón, P. (eds.) Advances in Artificial Life. Lecture Notes in Computer Science, pp. 704–720. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  15. 15.
    Komosinski, M.: The framsticks system: versatile simulator of 3d agents and their evolution. Kybernetes 32(1/2), 156–173 (2003)CrossRefGoogle Scholar
  16. 16.
    Lipson, H., Pollack, J.B.: Automatic design and manufacture of robotic lifeforms. Nature 406, 974–978 (2000)CrossRefGoogle Scholar
  17. 17.
    Long, J.: Darwin’s Devices: What Evolving Robots Can Teach Us About the History of Life and the Future of Technology. Basic Books, New York (2012)Google Scholar
  18. 18.
    Lund, H.: Co-evolving control and morphology with LEGO robots. In: Hara, F., Pfeifer, R. (eds.) Morpho-functional Machines: The New Species, pp. 59–79. Springer, Tokyo (2003)CrossRefGoogle Scholar
  19. 19.
    Nehmzow, U.: Physically embedded genetic algorithm learning in multi-robot scenarios: the pega algorithm. In: Prince, C.G., Demiris, Y., Marom, Y., Kozima, H., Balkenius, C. (eds.) Proceedings of The Second International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, Number 94 in Lund University Cognitive Studies, LUCS, Edinburgh, UK, August 2002Google Scholar
  20. 20.
    Pfeifer, R., Bongard, J.: How the Body Shapes the Way We Think. MIT Press, Cambridge (2006)Google Scholar
  21. 21.
    Simoes, E.D.V., Dimond, K.R.: An evolutionary controller for autonomous multi-robot systems. In: IEEE International Conference on Systems, Man, and Cybernetics, 1999. IEEE SMC 1999 Conference Proceedings, vol. 6, pp. 596–601. IEEE (1999)Google Scholar
  22. 22.
    Sims, K.: Evolving 3D morphology and behavior by competition. In: Artificial Life IV, pp. 28–39 (1994)Google Scholar
  23. 23.
    Sproewitz, A., Billard, A., Dillenbourg, P., Ijspeert, A.J.: Roombots - mechanical design of self-reconfiguring modular robots for adaptive furniture. In: IEEE International Conference on Robotics and Automation (ICRA 2009), pp. 4259–4264. IEEE (2009)Google Scholar
  24. 24.
    Usui, Y., Arita, T.: Situated and embodied evolution in collective evolutionary robotics. In: Proceedings of the 8th International Symposium on Artificial Life and Robotics, pp. 212–215 (2003)Google Scholar
  25. 25.
    Waibel, M., Floreano, D., Keller, L.: A quantitative test of Hamilton’s rule for the evolution of altruism. PLOS Biol. 9(5), e1000615 (2011)CrossRefGoogle Scholar
  26. 26.
    Watson, R., Ficici, S., Pollack, J.B.: Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robot. Auton. Syst. 39(1), 1–18 (2002)CrossRefGoogle Scholar
  27. 27.
    Weel, B., Crosato, E., Heinerman, J., Haasdijk, E., Eiben, A.E.: A robotic ecosystem with evolvable minds and bodies. In: 2014 IEEE International Conference on Evolvable Systems (ICES), pp. 165–172. IEEE (2014)Google Scholar
  28. 28.
    Zykov, V., Mytilinaios, E., Adams, B., Lipson, H.: Self-reproducing machines. Nature 435(7039), 163–164 (2005)CrossRefGoogle Scholar
  29. 29.
    Zykov, V., Mytilinaios, E., Desnoyer, M., Lipson, H.: Evolved and designed self-reproducing modular robotics. IEEE Trans. Robot. 23(2), 308–319 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.VU University AmsterdamAmsterdamThe Netherlands

Personalised recommendations