Abstraction as a Mechanism to Cross the Reality Gap in Evolutionary Robotics

  • Kirk Y. W. Scheper
  • Guido C. H. E. de Croon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9825)

Abstract

One of the major challenges of Evolutionary Robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction on the sensory inputs and motor actions as a potential solution to this problem. Abstraction means that the robot uses preprocessed sensory inputs and closed loop low-level controllers that execute higher level motor commands. We apply abstraction to the task of forming an asymmetric triangle with a homogeneous swarm of MAVs. The results show that the evolved behavior is effective both in simulation and reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Furthermore, we study the evolved solution, showing that it exploits the environment (in this case the identical behavior of the other robots) and creates behavioral attractors resulting in the creation of the required formation. Hence, the analysis suggests that by using abstraction, sensory-motor coordination is not necessarily lost but rather shifted to a higher level of abstraction.

References

  1. 1.
    Agmon, E., Beer, R.D.: The evolution and analysis of action switching in embodied agents. Adapt. Behav. 22(1), 3–20 (2013)CrossRefGoogle Scholar
  2. 2.
    Beer, R.D., Gallagher, J.C.: Evolving dynamical neural networks for adaptive behavior. Adapt. Behav. 1(1), 91–122 (1992)CrossRefGoogle Scholar
  3. 3.
    Bongard, J.C.: Evolutionary robotics. Commun. ACM 56(8), 74–83 (2013)CrossRefGoogle Scholar
  4. 4.
    Bongard, J.C., Zykov, V., Lipson, H.: Resilient machines through continuous self-modeling. Science 314(5802), 1118–1121 (2006)CrossRefGoogle Scholar
  5. 5.
    Cully, A., Clune, J., Tarapore, D., Mouret, J.B.: Robots that can adapt like animals. Nature 521(7553), 503–507 (2015). http://dx.doi.org/10.1038/nature14422, http://www.nature.com/nature/journal/v521/n7553/abs/nature14422.html#supplementary-information CrossRefGoogle Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S.M., Christensen, A.L.: Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE 11(3), 1–25 (2016)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd edn. Springer, Berlin (2015)CrossRefMATHGoogle Scholar
  10. 10.
    Floreano, D., Mondada, F.: Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot. In: Cliff, D., Husbands, P., Meyer, J.A., Wilson, S. (eds.) Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3, pp. 421–430. MIT Press, Cambridge (1994)Google Scholar
  11. 11.
    Floreano, D., Mondada, F.: Evolution of homing navigation in a real mobile robot. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26(3), 396–407 (1996)CrossRefGoogle Scholar
  12. 12.
    Hattenberger, G., Bronz, M., Gorraz, M.: Using the Paparazzi UAV system for scientific research. In: International Micro Air Vehicle Conference and Competition 2014, IMAV, Delft, Netherlands, pp. 247–252 (2014)Google Scholar
  13. 13.
    Izzo, D., Pettazzi, L.: Autonomous and distributed motion planning for satellite swarm. J. Guidance Control Dyn. 30(2), 449–459 (2007)CrossRefGoogle Scholar
  14. 14.
    Izzo, D., Simões, L.F., de Croon, G.C.H.E.: An evolutionary robotics approach for the distributed control of satellite formations. Evol. Intell. 7(2), 107–118 (2014)CrossRefGoogle Scholar
  15. 15.
    Jakobi, N.: Minimal simulations for evolutionary robotics. Ph.D. thesis, University of Sussex (1998)Google Scholar
  16. 16.
    Koos, S., Mouret, J.B., Doncieux, S.: The transferability approach: crossing the reality gap in evolutionary robotics. Trans. Evol. Comput. 17(1), 122–145 (2013)CrossRefGoogle Scholar
  17. 17.
    Lipson, H.: Evolutionary robotics: emergence of communication. Curr. Biol. 17(9), 129–155 (2007)CrossRefGoogle Scholar
  18. 18.
    Love, J.: Process Automation Handbook, 1st edn. Springer, London (2007). No. 800 in Production & Process EngineeringMATHGoogle Scholar
  19. 19.
    Natural Point Inc: Optitrack (2014). www.naturalpoint.com/optitrack/
  20. 20.
    Nolfi, S.: Power and limits of reactive agents. Neurocomputing 42, 119–145 (2002)CrossRefMATHGoogle Scholar
  21. 21.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence and Technology. MIT Press, Cambridge (2000)Google Scholar
  22. 22.
    Parrot: ARDrone 2. www.ardrone2.parrot.com/
  23. 23.
    Remes, B., Hensen, D., van Tienen, F., de Wagter, C., van der Horst, E., de Croon, G.: Paparazzi: how to make a swarm of Parrot AR Drones fly autonomously based on GPS. In: Proceedings of the International Micro Air Vehicle Conference and Flight Competition, IMAV, Toulouse, France, pp. 17–20 (2013)Google Scholar
  24. 24.
    Scheper, K.Y.W., Tijmons, S., de Visser, C.C., de Croon, G.C.H.E.: Behaviour trees for evolutionary robotics. Artif. Life 22(1), 23–48 (2016)CrossRefGoogle Scholar
  25. 25.
    Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kirk Y. W. Scheper
    • 1
  • Guido C. H. E. de Croon
    • 1
  1. 1.Micro Air Vehicle LaboratoryDelft University of TechnologyDelftThe Netherlands

Personalised recommendations