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Search Space Analysis of Evolvable Robot Morphologies

  • Karine Miras
  • Evert Haasdijk
  • Kyrre Glette
  • A. E. Eiben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)

Abstract

We present a study on morphological traits of evolved modular robots. We note that the evolutionary search space –the set of obtainable morphologies– depends on the given representation and reproduction operators and we propose a framework to assess morphological traits in this search space regardless of a specific environment and/or task. To this end, we present eight quantifiable morphological descriptors and a generic novelty search algorithm to produce a diverse set of morphologies for any given representation. With this machinery, we perform a comparison between a direct encoding and a generative encoding. The results demonstrate that our framework permits to find a very diverse set of bodies, allowing a morphological diversity investigation. Furthermore, the analysis showed that despite the high levels of diversity, a bias to certain traits in the population was detected. Surprisingly, the two encoding methods showed no significant difference in the diversity levels of the evolved morphologies or their morphological traits.

Keywords

Modular robots Evolutionary Robotics Morphology Generative encoding Novelty search 

Notes

Acknowledgements

This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 665347.

References

  1. 1.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-organizing Machines. MIT Press, Cambridge (2000)Google Scholar
  2. 2.
    Bongard, J.C.: Evolutionary robotics. Commun. ACM 56(8), 74–83 (2013)CrossRefGoogle Scholar
  3. 3.
    Vargas, P., Paolo, E.D., Harvey, I., Husbands, P. (eds.): The Horizons of Evolutionary Robotics. MIT Press, Cambridge (2014)Google Scholar
  4. 4.
    Doncieux, S., Bredeche, N., Mouret, J.B., Eiben, A.: Evolutionary robotics: what, why, and where to. Front. Robot. AI 2(4) (2015)Google Scholar
  5. 5.
    Sims, K.: Evolving 3D morphology and behavior by competition. Artif. Life 1(4), 353–372 (1994)CrossRefGoogle Scholar
  6. 6.
    Hornby, G.S., Pollack, J.B.: Evolving L-systems to generate virtual creatures. Comput. Graph. 25(6), 1041–1048 (2001)CrossRefGoogle Scholar
  7. 7.
    Samuelsen, E., Glette, K., Torresen, J.: A hox gene inspired generative approach to evolving robot morphology. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, pp. 751–758. ACM (2013)Google Scholar
  8. 8.
    Corucci, F., Calisti, M., Hauser, H., Laschi, C.: Novelty-based evolutionary design of morphing underwater robots. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 145–152. ACM (2015)Google Scholar
  9. 9.
    Veenstra, F., Faina, A., Risi, S., Stoy, K.: Evolution and morphogenesis of simulated modular robots: a comparison between a direct and generative encoding. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 870–885. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55849-3_56 CrossRefGoogle Scholar
  10. 10.
    Eiben, A., Bredeche, N., Hoogendoorn, M., Stradner, J., Timmis, J., Tyrrell, A., Winfield, A., et al.: The triangle of life: evolving robots in real-time and real-space. Adv. Artif. Life ECAL 2013, 1056–1063 (2013)Google Scholar
  11. 11.
    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
  12. 12.
    Auerbach, J., Aydin, D., Maesani, A., Kornatowski, P., Cieslewski, T., Heitz, G., Fernando, P., Loshchilov, I., Daler, L., Floreano, D.: Robogen: robot generation through artificial evolution. In: 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
  13. 13.
    Jacob, C.: Genetic L-system programming. In: Davidor, Y., Schwefel, H.-P., Manner, R. (eds.) Parallel Problem Solving from NaturePPSN III, pp. 333–343. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  14. 14.
    Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)CrossRefGoogle Scholar
  15. 15.
    Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: ALIFE, pp. 329–336 (2008)Google Scholar
  16. 16.
    Koza, J.R.: Genetic Programming: On The Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  17. 17.
    Stanley, K.O., D’Ambrosio, D.B., Gauci, J.: A hypercube-based encoding for evolving large-scale neural networks. Artif. Life 15(2), 185–212 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Karine Miras
    • 1
  • Evert Haasdijk
    • 1
  • Kyrre Glette
    • 2
  • A. E. Eiben
    • 1
  1. 1.Vrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.University of OsloOsloNorway

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