Evolutionary developmental robotics

Evolutionary developmental robotics (evo-devo-robo for short) refers to methodologies that systematically integrate evolutionary robotics, epigenetic robotics and morphogenetic robotics to study the evolution, physical and mental development and learning of natural intelligent systems in robotic systems. The field was formally suggested and fully discussed in a published paper[1] and further discussed in a published dialogue.[2]

The theoretical foundation of evo-devo-robo includes evolutionary developmental biology (evo-devo), evolutionary developmental psychology, developmental cognitive neuroscience etc. Further discussions on evolution, development and learning in robotics and design can be found in a number of papers,[3][4][5][6] including papers on hardware systems[7][8] and computing tissues.[9]

See also

References
  1. Y. Jin and Y. Meng, "Morphogenetic robotics: A new emerging field in developmental robotics. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Reviews and Applications, 41(2):145-160, 2011
  2. Y. Jin and Y. Meng. "Evolutionary Developmental Robotics: The Next Step to Go?" IEEE CIS AMD Newsletter, 8(1):13-14, 2011
  3. H. Lipson, Evolutionary robotics and open-ended design automation.
  4. J. Kodjabachian and J.-A. Meyer, Development, learning and evolution in animats. From Perception to Action, IEEE Press, 1994
  5. D. Floreano, and J. Urzelai. Neural morphogenesis, synaptic plasticity and evolution. Theory in Biosciences, 120(3-4):225-240, 2001
  6. J. Kodjabachian and J.-A. Meyer. Evolution and development of neural controllers for locomotion, gradient-following and obstacle avoidance in artificial insects. IEEE Trans. on Neural Networks, 9(5):796-812, 1998
  7. M. Sipper et al. A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems. IEEE Trans. on Evolutionary Computation. 1(1):83-97, 1997
  8. H. Guo, Y. Meng, and Y. Jin. A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. BioSystems, 98(3):193-203, 2009
  9. C. Teuscher, D. Mange, A. Stauffer, and G. Tempesti. Bio-inspired computing tissues: Towards machines that evolve, grow, and learn. IPCAT'2001, April 2001
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