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Out of Time: On the Constrains that Evolution in Hardware Faces When Evolving Modular Robots

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Applications of Evolutionary Computation (EvoApplications 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13224))

Abstract

With the recent advances of modular robots and low-cost manipulators, the evolution of robots, including morphologies and controllers, has become possible to perform in a physical setup without using any simulators. In this scenario, the evolution cannot be parallelized and the wall time becomes a scarce resource that should be used wisely. This paper analyses different algorithms by using the wall time as a stopping criterion for evolution, and it takes into account that wall time depends on the evaluation time plus the time to assemble and disassemble robots before and after an evaluation. The experiments have been performed in simulation, but the evaluation and assembly time have been carefully modelled from previous hardware experiments. Results suggest that (i) genetic algorithms are severely penalized, (ii) genetic algorithms can be improved by performing several evaluations of controllers for each morphology, and that (iii) evolutionary strategies that can chain several evaluations of robots with close morphologies can outperform other evolutionary algorithms. This finding is not surprising, but to the best of our knowledge previous attempts to evolve modular robots in hardware have not employed evolutionary strategies.

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Notes

  1. 1.

    More information about the Emerge robot can be found at https://sites.google.com/view/emergemodular.

References

  1. Auerbach, J., et al.: RoboGen: robot generation through artificial evolution. In: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, Artificial Life, pp. 136–137. The MIT Press, New York (2014). https://doi.org/10.7551/978-0-262-32621-6-ch022

  2. Brodbeck, L., Hauser, S., Iida, F.: Morphological evolution of physical robots through model-free phenotype development. PLoS ONE 10(6), 1–17 (2015). https://doi.org/10.1371/journal.pone.0128444

    Article  Google Scholar 

  3. Caamaño, P., Tedín, R., Paz-Lopez, A., Becerra, J.A.: JEAF: a Java evolutionary algorithm framework. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)

    Google Scholar 

  4. Cheney, N., Bongard, J., SunSpiral, V., Lipson, H.: Scalable co-optimization of morphology and control in embodied machines. J. Roy. Soc. Interface 15(143), 20170937 (2018)

    Article  Google Scholar 

  5. Chocron, O.: Evolutionary design of modular robotic arms. Robotica 26(3), 323–330 (2008). https://doi.org/10.1017/S0263574707003931

    Article  Google Scholar 

  6. Cully, A., Clune, J., Tarapore, D., Mouret, J.B.: Robots that can adapt like animals. Nature 521(7553), 503–507 (2015). https://doi.org/10.1038/nature14422

    Article  Google Scholar 

  7. Faíña, A., Bellas, F., López-Peña, F., Duro, R.J.: EDHMoR: evolutionary designer of heterogeneous modular robots. Eng. Appl. Artif. Intell. 26(10), 2408–2423 (2013). https://doi.org/10.1016/j.engappai.2013.09.009

    Article  Google Scholar 

  8. Goff, L.K.L., et al.: Morpho-evolution with learning using a controller archive as an inheritance mechanism. arXiv:2104.04269 [cs], September 2021

  9. Hale, M.F., et al.: Hardware design for autonomous robot evolution. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2140–2147, December 2020. https://doi.org/10.1109/SSCI47803.2020.9308204

  10. Hale, M.F., et al.: The ARE robot fabricator: how to (re)produce robots that can evolve in the real world. In: The 2019 Conference on Artificial Life, pp. 95–102. MIT Press, Cambridge (2019). https://doi.org/10.1162/isal_a_00147.xml

  11. Jelisavcic, M., Glette, K., Haasdijk, E., Eiben, A.E.: Lamarckian evolution of simulated modular robots. Front. Robot. AI 6, 9 (2019). https://doi.org/10.3389/frobt.2019.00009

    Article  Google Scholar 

  12. Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32(11), 1238–1274 (2013). https://doi.org/10.1177/0278364913495721

    Article  Google Scholar 

  13. Koos, S., Mouret, J.B., Doncieux, S.: The transferability approach: crossing the reality gap in evolutionary robotics. IEEE Trans. Evol. Comput. 17(1), 122–145 (2013). https://doi.org/10.1109/TEVC.2012.2185849

    Article  Google Scholar 

  14. Lan, G., De Carlo, M., van Diggelen, F., Tomczak, J.M., Roijers, D.M., Eiben, A.E.: Learning directed locomotion in modular robots with evolvable morphologies. Appl. Soft Comput. 111 (2021). https://doi.org/10.1016/j.asoc.2021.107688

  15. Lipson, H., Sunspiral, V., Bongard, J., Cheney, N.: On the difficulty of co-optimizing morphology and control in evolved virtual creatures. In: Artificial Life Conference Proceedings 13, pp. 226–233. MIT Press (2016)

    Google Scholar 

  16. Marbach, D., Ijspeert, A.J.: Online optimization of modular robot locomotion. In: IEEE International Conference Mechatronics and Automation, vol. 1, pp. 248–253. IEEE (2005)

    Google Scholar 

  17. Moreno, R., et al.: Automated reconfiguration of modular robots using robot manipulators. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 884–891, November 2018. https://doi.org/10.1109/SSCI.2018.8628628

  18. Moreno, R., Faina, A.: Reusability vs morphological space in physical robot evolution. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1389–1391 (2020)

    Google Scholar 

  19. Moreno, R., Faiña, A.: EMERGE modular robot: a tool for fast deployment of evolved robots. Front. Robot. AI 8, 198 (2021). https://doi.org/10.3389/frobt.2021.699814

    Article  Google Scholar 

  20. Mouret, J.B., Clune, J.: Illuminating search spaces by mapping elites. arXiv:1504.04909 [cs, q-bio], April 2015

  21. Nolfi, S., Bongard, J., Husbands, P., Floreano, D.: Evolutionary robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 2035–2068. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32552-1_76

    Chapter  Google Scholar 

  22. Nordmoen, J., Nygaard, T.F., Samuelsen, E., Glette, K.: On restricting real-valued genotypes in evolutionary algorithms. In: Castillo, P.A., Jiménez Laredo, J.L. (eds.) EvoApplications 2021. LNCS, vol. 12694, pp. 3–16. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72699-7_1

    Chapter  Google Scholar 

  23. Nordmoen, J., Veenstra, F., Ellefsen, K.O., Glette, K.: MAP-elites enables powerful stepping stones and diversity for modular robotics. Front. Robot. AI 8, 56 (2021). https://doi.org/10.3389/frobt.2021.639173

    Article  Google Scholar 

  24. Nygaard, T.F., Martin, C.P., Samuelsen, E., Torresen, J., Glette, K.: Real-world evolution adapts robot morphology and control to hardware limitations. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2018, pp. 125–132. Association for Computing Machinery, New York, July 2018. https://doi.org/10.1145/3205455.3205567

  25. Nygaard, T.F., Martin, C.P., Torresen, J., Glette, K., Howard, D.: Real-world embodied AI through a morphologically adaptive quadruped robot. Nat. Mach. Intell. 3(5), 410–419 (2021). https://doi.org/10.1038/s42256-021-00320-3

    Article  Google Scholar 

  26. Rohmer, E., Singh, S.P.N., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: IROS 2013, pp. 1321–1326. IEEE, Tokyo, November 2013. https://doi.org/10.1109/IROS.2013.6696520

  27. Sims, K.: Evolving virtual creatures. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH 1994, pp. 15–22. ACM Press, New York (1994). https://doi.org/10.1145/192161.192167

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Authors and Affiliations

  1. IT University of Copenhagen, Copenhagen, Denmark

    Rodrigo Moreno & Andres Faiña

Authors
  1. Rodrigo Moreno
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  2. Andres Faiña
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Correspondence to Rodrigo Moreno .

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Editors and Affiliations

  1. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  2. Universidad Complutense de Madrid, Madrid, Spain

    J. Ignacio Hidalgo

  3. Edinburgh Napier University, Edinburgh, UK

    Kehinde Oluwatoyin Babaagba

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Moreno, R., Faiña, A. (2022). Out of Time: On the Constrains that Evolution in Hardware Faces When Evolving Modular Robots. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_42

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  • DOI: https://doi.org/10.1007/978-3-031-02462-7_42

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  • Online ISBN: 978-3-031-02462-7

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