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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agmon, E., Beer, R.D.: The evolution and analysis of action switching in embodied agents. Adapt. Behav. 22(1), 3–20 (2013)
Beer, R.D., Gallagher, J.C.: Evolving dynamical neural networks for adaptive behavior. Adapt. Behav. 1(1), 91–122 (1992)
Bongard, J.C.: Evolutionary robotics. Commun. ACM 56(8), 74–83 (2013)
Bongard, J.C., Zykov, V., Lipson, H.: Resilient machines through continuous self-modeling. Science 314(5802), 1118–1121 (2006)
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
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)
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)
Eiben, A.E., Kernbach, S., Haasdijk, E.: Embodied artificial evolution: artificial evolutionary systems in the 21st Century. Evol. Intell. 5(4), 261–272 (2012)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd edn. Springer, Berlin (2015)
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)
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)
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)
Izzo, D., Pettazzi, L.: Autonomous and distributed motion planning for satellite swarm. J. Guidance Control Dyn. 30(2), 449–459 (2007)
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)
Jakobi, N.: Minimal simulations for evolutionary robotics. Ph.D. thesis, University of Sussex (1998)
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)
Lipson, H.: Evolutionary robotics: emergence of communication. Curr. Biol. 17(9), 129–155 (2007)
Love, J.: Process Automation Handbook, 1st edn. Springer, London (2007). No. 800 in Production & Process Engineering
Natural Point Inc: Optitrack (2014). www.naturalpoint.com/optitrack/
Nolfi, S.: Power and limits of reactive agents. Neurocomputing 42, 119–145 (2002)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence and Technology. MIT Press, Cambridge (2000)
Parrot: ARDrone 2. www.ardrone2.parrot.com/
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)
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)
Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Scheper, K.Y.W., de Croon, G.C.H.E. (2016). Abstraction as a Mechanism to Cross the Reality Gap in Evolutionary Robotics. In: Tuci, E., Giagkos, A., Wilson, M., Hallam, J. (eds) From Animals to Animats 14. SAB 2016. Lecture Notes in Computer Science(), vol 9825. Springer, Cham. https://doi.org/10.1007/978-3-319-43488-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-43488-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43487-2
Online ISBN: 978-3-319-43488-9
eBook Packages: Computer ScienceComputer Science (R0)