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A baseline-realistic objective open-ended kinematics simulator for evolutionary robotics

Published:15 July 2017Publication History

ABSTRACT

Most modern applications of Evolutionary Robotics (ER) rely upon computer-based physics simulations in order to model the behavior of the systems in question. One of the greatest challenges in the field of ER, therefore, is the development of robust, high-precision and accurate physics simulators that can model all necessary and relevant real-world interactions in an computationally efficient manner. Up until now, most popular ER simulators are nonetheless deficient in one or many of these properties. Here we introduce a new competitive simulator, the Baseline-Realistic Objective Open-Ended Kinematics Simulator (BROOKS) that outperforms other off-the-shelf simulators in most criteria. Our simulator is free, open-sourced, and easy to modify. It can model a wide range of robotic platforms, substrates and environments. Moreover, we claim solutions produced within the BROOKS simulator perform almost identically in the real-world, thereby helping to address one of the most challenging aspects of simulation in Evolutionary Robotics: the Reality Gap. Ultimately, we believe that BROOKS will establish a new baseline against which all other simulators should be compared.

References

  1. Josh Bongard, Victor Zykov, and Hod Lipson. 2006. Resilient machines through continuous self-modeling. Science 314, 5802 (2006), 1118--1121.Google ScholarGoogle ScholarCross RefCross Ref
  2. Josh C Bongard and Rolf Pfeifer. 2003. Evolving complete agents using artificial ontogeny. In Morpho-functional Machines: The new species. Springer, 237--258.Google ScholarGoogle Scholar
  3. Rodney A Brooks. 1990. Elephants don't play chess. Robotics and autonomous systems 6, 1--2 (1990), 3--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Ken Caluwaerts, Jérémie Despraz, Atil Işçen, Andrew P Sabelhaus, Jonathan Bruce, Benjamin Schrauwen, and Vytas SunSpiral. 2014. Design and control of compliant tensegrity robots through simulation and hardware validation. Journal of The Royal Society Interface 11, 98 (2014), 20140520.Google ScholarGoogle ScholarCross RefCross Ref
  5. Dave Cliff, Philip Husbands, and Inman Harvey. 1993. Evolving visually guided robots. In From Animals to Animats 2. Proceedings of the Second International Conference on Simulation of Adaptive Behavior, Jean-Arcady Meyer, Herbert L Roitblat, and Stewart W. Wilson (Eds.). MIT Press, Cambridge MA, 374--383. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. 2015. Robots that can adapt like animals. Nature 521, 7553 (2015), 503--507.Google ScholarGoogle Scholar
  7. Dario Floreano and Francesco Mondada. 1994. Automatic creation of an autonomous agent: Genetic evolution of a neural network driven robot. In From Animals to Animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, Dave Cliff, Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson (Eds.). MIT Press, 421--430. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Kyrre Glette and Mats Hovin. 2010. Evolution of artificial muscle-based robotic locomotion in PhysX. In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on. IEEE, 1114--1119.Google ScholarGoogle ScholarCross RefCross Ref
  9. Inman Harvey, Phil Husbands, and Dave Cliff. 1994. Seeing the light: artificial evolution, real vision. In From Animals to Animats 3:Proceedings of the Third International Conference on Simulation of Adaptive Behavior, Dave Cliff, Philip Husbands, Jean-Arcady Meyer, and Stewart W. Wilson (Eds.). MIT Press, Cambridge MA, 392--401. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Jonathan Hiller and Hod Lipson. 2012. Automatic design and manufacture of soft robots. IEEE Transactions on Robotics 28, 2 (2012), 457--466. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Gregory S Hornby and Jordan B Pollack. 2001. Body-brain co-evolution using L-systems as a generative encoding. In Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation. Morgan Kaufmann Publishers Inc., 868--875. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Nick Jakobi, Phil Husbands, and Inman Harvey. 1995. Noise and the reality gap: The use of simulation in evolutionary robotics. Advances in artificial life (1995), 704--720. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Jean-Baptiste Mouret, Sylvain Koos, and Stéphane Doncieux. 2013. Crossing the reality gap: a short introduction to the transferability approach. arXiv preprint arXiv.1307.1870 (2013).Google ScholarGoogle Scholar
  14. John Rieffel, Francisco Valero-Cuevas, and Hod Lipson. 2009. Automated discovery and optimization of large irregular tensegrity structures. Computers & Structures 87, 5 (2009), 368--379. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. John A Rieffel, Francisco J Valero-Cuevas, and Hod Lipson. 2010. Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotion. Journal of the royal society interface 7, 45 (2010), 613--621.Google ScholarGoogle ScholarCross RefCross Ref
  16. Karl Sims. 1994. Evolving virtual creatures. In Proceedings of the 21st annual conference on Computer graphics and interactive techniques. ACM, 15--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Adrian Thompson. 1996. An evolved circuit, intrinsic in silicon, entwined with physics. In Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware, Tetsuya Higuchi, Masaya Iwata, and Weixin Liu (Eds.). Springer, 390--405. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Richard A Watson, Sevan G Ficici, and Jordan B Pollack. 2002. Embodied evolution: Distributing an evolutionary algorithm in a population of robots. Robotics and Autonomous Systems 39, 1 (2002), 1--18.Google ScholarGoogle Scholar
  19. Viktor Zykov, Josh Bongard, and Hod Lipson. 2004. Evolving dynamic gaits on a physical robot. In Proceedings of Genetic and Evolutionary Computation Conference (GECCO), Late Breaking Paper, GECCO, K. Deb, R. Poli, W. Banzhaf, H.-G. Beyer, E. Burke, P. Darwen, D. Dasgupta, D. Floreano, J. Foster, M. Harman, O. Holland, P.L. Lanzi, L. Spector, A.G.B. Tettamanzi, D. Thierens, and A. Tyrrell (Eds.), Vol. 4. Springer.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Conferences
    GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2017
    1934 pages
    ISBN:9781450349390
    DOI:10.1145/3067695

    Copyright © 2017 ACM

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    • Published: 15 July 2017

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