Evolutionary robotics: Model or design?

Frontiers in Robotics and AI

Volumn 1, Issue , 2014, Pages

  Trianni, Vito ^a  

^a INSTITUTE OF COGNITIVE SCIENCES AND TECHNOLOGIES   (Italy)

Author keywords

Artificial life; Complex systems; Design methodology; Evolutionary robotics; Modeling

Indexed keywords

EID: 84975859774     PISSN: None     EISSN: 22969144     Source Type: Journal    
DOI: 10.3389/frobt.2014.00013     Document Type: Article

References (55)

1. Adami, C. (2006). Digital genetics: unravelling the genetic basis of evolution. Nat. Rev. Genet. 7, 109–118. doi:10.1038/nrg1771
2. Ampatzis, C., Tuci, E., Trianni, V., and Dorigo, M. (2008). Evolution of signaling in a multi-robot system: categorization and communication. Adapt. Behav. 16, 5–26. doi:10.1177/1059712307087282
3. Auerbach, J., and Bongard, J. (2014). Environmental influence on the evolution of morphological complexity in machines. PLoS Comput. Biol. 10:e1003399. doi:10.1371/journal.pcbi.1003399
4. Auvray, M., Lenay, C., and Stewart, J. (2009). Perceptual interactions in a minimalist virtual environment. New Ideas Psychol. 27, 32–47. doi:10.1016/j.newideapsych. 2007.12.002
5. Baldassarre,G.,Trianni,V.,Bonani,M.,Mondada,F.,Dorigo,M.,and Nolfi,S. (2007). Self-organized coordinated motion in groups of physically connected robots. IEEE Trans. Syst. Man Cybern. B Cybern. 37, 224–239. doi:10.1109/TSMCB.2006. 881299
6. Barandiaran, X. (2006). On what makes certain dynamical systems cognitive: a minimally cognitive organization program. Adapt. Behav. 14, 171–185. doi:10.1177/105971230601400208
7. Beer, R. (2003). The dynamics of active categorical perception in an evolved model agent. Adapt. Behav. 11, 209–243. doi:10.1016/j.neunet.2009.03.002
8. Bongard, J. (2010). The utility of evolving simulated robot morphology increases with task complexity for object manipulation. Artif. Life 16, 201–223. doi:10. 1162/artl.2010.Bongard.024
9. Bongard, J., and Lipson, H. (2014). Evolved machines shed light on robustness and resilience. Proc. IEEE 102, 899–914. doi:10.1109/JPROC.2014.2312844
10. Bongard, J., Zykov,V., and Lipson, H. (2006). Resilient machines through continuous self-modeling. Science 314, 1118–1121. doi:10.1126/science.1133687
11. Bongard, J. C. (2013). Evolutionary robotics. Commun. ACM 56, 74–83. doi:10. 1145/2492007.2493883
12. Bredeche, N., Montanier, J.-M., Liu, W., and Winfield, A. (2012). Environmentdriven distributed evolutionary adaptation in a population of autonomous robotic agents. Math. Comput. Model. Dyn. Syst. 18, 101–129. doi:10.1080/13873954.2011.601425
13. Buhrmann, T., Di Paolo, E., and Barandiaran, X. (2013). A dynamical systems account of sensorimotor contingencies. Front. Psychol. 4:285. doi:10.3389/fpsyg. 2013.00285
14. Clune, J., Stanley, K., Pennock, R., and Ofria, C. (2011). On the performance of indirect encoding across the continuum of regularity. IEEE Trans. Evol. Comput. 15, 346–367. doi:10.1109/TEVC.2010.2104157
15. Dale, K., and Husbands, P. (2010). The evolution of reaction-diffusion controllers for minimally cognitive agents. Artif. Life 16, 1–19. doi:10.1162/artl.2009.16.1.16100
16. Doncieux, S., and Mouret, J.-B. (2014). Beyond black-box optimization: a review of selective pressures for evolutionary robotics. Evol. Intell. 7, 71–93. doi:10.1007/s12065-014-0110-x
17. Elfwing, S., and Doya, K. (2014). Emergence of polymorphic mating strategies in robot colonies. PLoS ONE 9:e93622. doi:10.1371/journal.pone.0093622
18. Fehérvári, I., Trianni, V., and Elmenreich, W. (2013). “On the effects of the robot configuration on evolving coordinated motion behaviors,” in Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC) (Cancun: IEEE), 1209–1216. Available from: http://ieeexplore.ieee.org/xpl/articleDetails. jsp?arnumber=6557703
19. Floreano, D., Husbands, P., and Nolfi, S. (2008). “Evolutionary robotics,” in Springer Handbook of Robotics, Chapter 61, eds B. Siciliano and O. Khatib (Berlin: Springer), 1423–1451.
20. Floreano, D., and Keller, L. (2010). Evolution of adaptive behaviour in robots by means of Darwinian selection. PLoS Biol. 8:e1000292. doi:10.1371/journal.pbio. 1000292
21. Francesca, G., Brambilla, M., Brutschy, A., Trianni,V., and Birattari, M. (2014). Automode: a novel approach to the automatic design of control software for robot swarms. Swarm Intell. 8, 89–112. doi:10.1007/s11721-014-0092-4
22. Froese, T., and Di Paolo, E. (2010). Modelling social interaction as perceptual crossing: an investigation into the dynamics of the interaction process. Conn. Sci. 22, 43–68. doi:10.1080/09540090903197928
23. Haasdijk, E., Bredeche, N., and Eiben, A. (2014). Combining environment-driven adaptation and task-driven optimisation in evolutionary robotics. PLoS ONE 9:e98466. doi:10.1371/journal.pone.0098466
24. Hamilton, W. (1964). The genetical evolution of social behaviour. i+ii. J. Theor. Biol. 7, 1–52. doi:10.1016/0022-5193(64)90039-6
25. Harvey, I., Di Paolo, E., Wood, R., Quinn, M., and Tuci, E. (2005). Evolutionary robotics: a new scientific tool for studying cognition. Artif. Life 11, 79–98. doi:10.1162/1064546053278991
26. Hiller, J., and Lipson, H. (2012). Automatic design and manufacture of soft robots. IEEE Trans. Robot. 28, 457–466. doi:10.1109/TRO.2011.2172702
27. Iizuka, H., Ando, H., and Maeda, T. (2013). Extended homeostatic adaptation model with metabolic causation in plasticity mechanism–toward constructing a dynamic neural network model for mental imagery. Adapt. Behav. 21, 263–273. doi:10.1177/1059712313488426
28. Koos, S., Mouret, J., and Doncieux, S. (2013). The transferability approach: crossing the reality gap in evolutionary robotics. IEEE Trans. Evol. Comput. 17, 122–145. doi:10.1109/TEVC.2012.2185849
29. Lehman, J., and Stanley, K. (2011). Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19, 189–223. doi:10.1162/EVCO_ a_00025
30. Lichocki, P., Wischmann, S., Keller, L., and Floreano, D. (2013). Evolving team compositions by agent swapping. IEEE Trans. Evol. Comput. 17, 282–298. doi:10.1109/TEVC.2012.2191292
31. Lipson, H. (2005). Evolutionary robotics and open-ended design automation. Biomimetics 17, 129–155. doi:10.1201/9781420037715.ch4
32. Matsuda, E., Hubert, J., and Ikegami, T. (2014). A robotic approach to understanding the role and the mechanism of vicarious trial-and-error in a t-maze task. PLoS ONE 9:e102708. doi:10.1371/journal.pone.0102708
33. Meyer, J.-A., and Wilson, S. (eds) (1991). From Animals to Animats: Proceedings of the First International Conference on the Simulation of Adaptive Behaviour. Cambridge, MA: MIT Press.
34. Mitri, S., Floreano, D., and Keller, L. (2011). Relatedness influences signal reliability in evolving robots. Proc. R. Soc. Lond. B Biol. Sci. 278, 378. doi:10.1098/rspb. 2010.1407
35. Mitri, S., Wischmann, S., Floreano, D., and Keller, L. (2012). Using robots to understand social behaviour. Biol. Rev. 88, 31–39. doi:10.1111/j.1469-185X.2012. 00236.x
36. Morlino, G., Gianelli, C., Borghi, A., and Nolfi, S. (2014). Learning to manipulate and categorize in human and artificial agents. Cogn. Sci. doi:10.1111/cogs.12130
37. Mouret, J., and Doncieux, S. (2012). Encouraging behavioral diversity in evolutionary robotics: an empirical study. Evol. Comput. 20, 91–133. doi:10.1162/EVCO_ a_00048
38. Nelson, A., Barlow, G., and Doitsidis, L. (2009). Fitness functions in evolutionary robotics: a survey and analysis. Rob. Auton. Syst. 57, 345–370. doi:10.1016/j.robot. 2008.09.009
39. Olson, R., Hintze, A., Dyer, F., Knoester, D., and Adami, C. (2013). Predator confusion is sufficient to evolve swarming behaviour. J. R. Soc. Interface 10, 20130305–20130305. doi:10.1098/rsif.2013.0305
40. Paul, C. (2006). Morphological computation. Rob. Auton. Syst. 54, 619–630. doi:10.1016/j.robot.2006.03.003
41. Paul, C., Valero-Cuevas, F., and Lipson, H. (2006). Design and control of tensegrity robots for locomotion. IEEE Trans. Robot. 22, 944–957. doi:10.1098/rsif.2014. 0520
42. Pfeifer, R., Lungarella, M., and Iida, F. (2007). Self-organization, embodiment, and biologically inspired robotics. Science 318, 1088–1093. doi:10.1126/science. 1145803
43. Sellers, W., Cain, G., Wang, W., and Crompton, R. (2005). Stride lengths, speed and energy costs in walking of Australopithecus afarensis: using evolutionary robotics to predict locomotion of early human ancestors. J. R. Soc. Interface 2, 431–441. doi:10.1098/rsif.2005.0060
44. Sellers, W., Margetts, L., Coria, R., and Manning, P. (2013). March of the titans: the locomotor capabilities of Sauropod dinosaurs. PLoS ONE 8:e78733. doi:10. 1371/journal.pone.0078733
45. Seth, A. (2007). The ecology of action selection: insights from artificial life. Philos. Trans. R. Soc. B Biol. Sci. 362, 1545–1558. doi:10.1098/rstb.2007.2052
46. Siegwart, R., Nourbakhsh, I., and Scaramuzza, D. (2011). Introduction to Autonomous Mobile Robots. Cambridge, MA: MIT Press.
47. Stanley, K. O. (2011). “Why evolutionary robotics will matter,” in New Horizons in Evolutionary Robotics (Berlin, Heidelberg: Springer Berlin Heidelberg), 37–41.
48. Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics. Cambridge, MA: MIT Press.
49. Trianni, V., and López-Ibáñez, M. (2014). Advantages of Multi-Objective Optimisation in Evolutionary Robotics: Survey and Case Studies. Brussels.
50. Trianni, V., and Nolfi, S. (2009). Self-organizing sync in a robotic swarm: a dynamical system view. IEEE Trans. Evol. Comput. 13, 722–741. doi:10.1109/TEVC.2009. 2015577
51. sitionality in evolving robots. IEEE Trans. Auton. Ment. Dev. 3, 176–189. doi:10.1109/TAMD.2011.2114659 Vickerstaff, R., and Di Paolo, E. (2005). Evolving neural models of path integration. J. Exp. Biol. 208, 3349–3366. doi:10.1242/jeb.01772
52. Waibel, M., Floreano, D., and Keller, L. (2011). A quantitative test of Hamilton’s rule for the evolution of altruism. PLoS Biol. 9:e1000615. doi:10.1371/journal.pbio. 1000615
53. Waibel, M., Keller, L., and Floreano, D. (2009). Genetic team composition and level of selection in the evolution of cooperation. IEEE Trans. Evol. Comput. 13, 648–660.
54. doi:10.1109/TEVC.2008.2011741 Webb, B. (2009). Animals versus animats: or why not model the real iguana? Adapt. Behav. 17, 269–286. doi:10.1177/1059712309339867
55. Wischmann, S., Floreano, D., and Keller, L. (2012). Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots. Proc. Natl. Acad. Sci. U. S. A. 109, 864–868. doi:10.1073/pnas.1104267109