Abstract
In this paper we aim to develop a controller that allows a robot to traverse an structured environment. The approach we use is to decompose the environment into simple sub-environments that we use as basis for evolving the controller. Specifically, we decompose a narrow corridor environment into four different sub-environments and evolve controllers that generalize to traverse two larger environments composed of the sub-environments. We also study two strategies for presenting the sub-environments to the evolutionary algorithm: all sub-environments at the same time and in sequence. Results show that by using a sequence the evolutionary algorithm can find a controller that performs well in all sub-environments more consistently than when presenting all sub-environments together. We conclude that environment decomposition is an useful approach for evolving controllers for structured environments and that the order in which the decomposed sub-environments are presented in sequence impacts the performance of the evolutionary algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Crespi, A., Lachat, D., Pasquier, A., Ijspeert, A.J.: Controlling swimming and crawling in a fish robot using a central pattern generator. Auton. Robots 25(1–2), 3–13 (2008)
Lee, W.P., Hallam, J., Lund, H.H.: Learning complex robot behaviours by evolutionary computing with task decomposition. In: Birk, A., Demiris, J. (eds.) Learning Robots. LNCS, vol. 1545, pp. 155–172. Springer, Heidelberg (1998)
Whiteson, S., Kohl, N., Miikkulainen, R., Stone, P.: Evolving soccer keepaway players through task decomposition. Mach. Learn. 59(1–2), 5–30 (2005)
Lessin, D., Fussell, D., Miikkulainen, R.: Open-ended behavioral complexity for evolved virtual creatures. In: Proceedings of the GECCO 2013, p. 335. ACM Press, New York, USA (2013)
Rossi, C., Eiben, A.E.: Simultaneous versus incremental learning of multiple skills by modular robots. Evol. Intell. 7(2), 119–131 (2014)
Stone, P., Veloso, M.M.: Layered learning. In: de Mantaras, R.L., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 369–381. Springer, Heidelberg (2000)
Gomez, F., Miikkulainen, R.: Incremental evolution of complex general behavior. Adapt. Behav. 5(3–4), 317–342 (1997)
Bongard, J.: Behavior chaining: incremental behavioral integration for evolutionary robotics. Artif. Life XI Number 1976, 64–71 (2008)
Auerbach, J., Bongard, J.C.: How robot morphology and training order affect the learning of multiple behaviors. In: IEEE Congress on CEC 2009, pp. 39–46, Trondheim, May 2009
Bongard, J.C.: Morphological and environmental scaffolding synergize when evolving robot controllers. In: GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms, p. 179. ACM Press, Dublin, Ireland (2011)
Mukosaka, N., Tanev, I., Shimohara, K.: Performance of incremental genetic programming on adaptability of snake-like Robot. IES2013 24, 152–157 (2013)
Kuyucu, T., Tanev, I., Shimohara, K.: Genetic transposition inspired incremental genetic programming for efficient coevolution of locomotion and sensing of simulated snake-like robot. In: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems ECAL-2011, pp. 439–446. MIT Press, Paris (2011)
Song, G.B., Cho, S.B.: Combining incrementally evolved neural networks based on cellular automata for complex adaptive behaviors. In: First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, pp. 121–129. IEEE, San Antonio, TX (2000)
Mouret, J.-B., Doncieux, S.: Incremental Evolution of animats’ behaviors as a multi-objective optimization. In: Asada, M., Hallam, J.C.T., Meyer, J.-A., Tani, J. (eds.) SAB 2008. LNCS (LNAI), vol. 5040, pp. 210–219. Springer, Heidelberg (2008)
Rohmer, E., Singh, S.P.N., Freese, M.: V-REP: A versatile and scalable robot simulation framework. In: 26th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pp. 1321–1326. IEEE, Tokyo, November 2013
Jantapremjit, P., Austin, D.: Design of a modular self-reconfigurable robot. In: Australian Conference on Robotics and Automation. Citeseer, Sydney, Australia (2001)
Moreno, R., Gomez, J.: Simple chain type modular robot hardware (2011). https://www.youtube.com/watch?v=x6UQfC4KALA
Storn, R., Price, K.: Differential evolutiona simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Caamano, P., Tedin, R., Paz-Lopez, A., Becerra, J.A.: JEAF: A Java Evolutionary Algorithm Framework. In: IEEE Congress on Evolutionary Computation, CEC 2010, pp. 1–8, December 2007. IEEE, Barcelona, July 2010
Jakobi, N.: Evolutionary robotics and the radical envelope-of-noise hypothesis. Adapt. Behav. 6(2), 325–368 (1997)
Acknowledgments
This project is supported in part by grant 23418 of the program “Programa nacional de proyectos para el fortalecimiento de la investigación, la creación y la innovación en posgrados en la Universidad Nacional de Colombia 2013” of Universidad Nacional de Colombia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Moreno, R., Faiña, A., Støy, K. (2015). Evolving Robot Controllers for Structured Environments Through Environment Decomposition. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_64
Download citation
DOI: https://doi.org/10.1007/978-3-319-16549-3_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16548-6
Online ISBN: 978-3-319-16549-3
eBook Packages: Computer ScienceComputer Science (R0)