This talk discusses a radically unconventional approach to developing robots: using evolution. Artificial evolution has been successful in optimization and design in the field of Evolutionary Computing; applying it to developing robots implies huge challenges and great promises, [1], [2], [3]. The long-term vision foresees robots that reproduce and evolve in real-time and real space. Possible application scenarios can be divided into two categories depending on the extent of human involvement. First, the “breeding farm scenario”, where humans steer and accelerate evolution through influencing selection (and possibly also reproduction) until a good robot design emerges in the environment of the “breeding farm”. This design can then be validated, produced and employed on a large scale in the real application environment, e.g., cave systems, deep seas, rainforests. Second, the “other planet scenario”, where the robot population evolves and adapts autonomously to the unknown environment without the need for direct human oversight. In this case evolution is not merely a design method that stops when the outcome is satisfactory, but a permanent force that continually improves and adapts the population to the given circumstances.
While at the current level of technology this may sound more fiction than science, the quick development of 3D-printing and autonomous assembly can make this a feasible option in the near future. As of today, the overall system architecture has been designed [4], and the first proofs-of concept with automated robot (re)production have been published [5], [6]. To illustrate specific aspects I will reflect on an ongoing EPSRC project that is developing the first evolutionary robot system where robots evolve in a breeding farm scenario [7].
[1] A.E. Eiben, S. Kernbach, and Evert Haasdijk, Embodied artificial evolution: Artificial evolutionary systems in the 21st Century, Evolutionary Intelligence, 5(4):261-272, 2012.
[2] A.E. Eiben and J. Smith, From evolutionary computation to the evolution of things, Nature, 521:476-482, 2015.
[3] D. Howard, A.E. Eiben, D.F. Kennedy, J.-B. Mouret, P. Valencia, and D. Winkler, Evolving embodied intelligence from materials to machines, Nature Machine Intelligence 1(1):12-19, January 2019, doi: 10.1038/s42256-018-0009-9
[4] A.E. Eiben, N. Bredeche, M. Hoogendoorn, J. Stradner, J. Timmis, A.M. Tyrrell and A. Winfield, The Triangle of Life: Evolving Robots in Real-time and Real-space, Proceedings ECAL 2013, MIT Press, 2013, pp. 1056-1063.
[5] Brodbeck, L., Hauser, S., & Iida, F. (2015). Morphological evolution of physical robots through model-free phenotype development. PLoS One, 10(6), e0128444.
[6] M.J. Jelisavcic, M. De Carlo, E. Hupkes, P. Eustratiadis, J. Orlowski, E. Haasdijk, J. Auerbach, and A.E. Eiben, Real-World Evolution of Robot Morphologies: A Proof of Concept, Artificial life. 23(2):206-235, 2017.
[7] M.F. Hale, Edgar Buchanan, A.F. Winfield, J. Timmis, E. Hart, A.E. Eiben, M. Angus, F. Veenstra, W. Li, R. Woolley, M. De Carlo, A.M. Tyrrell, The ARE Robot Fabricator: How to (Re)produce Robots that Can Evolve in the Real World, Proceedings ALIFE 2019, MIT Press, 2019, to appear
