The question of how to evolve real robot bodies and why we don't appear to have made much progress in the last 15 years was the subject of my keynote at the IEEE International Conference on Evolvable Systems (ICES 2014) in Orlando, a week ago. Here are my slides:
The talk was in three parts.
In part one I outlined the basic approach to evolving robots using the genetic algorithm, referring to figure 18: The four-stage process of Evolutionary Robotics, from chapter 5 of my book:
RoboGen, Golem and, as far as I'm aware, all work on evolving real physical robot bodies to date has used the simulate-then-transfer-to-real approach, in which the whole evolutionary process - including fitness testing - takes place in simulation and only the final 'fittest' robot is physically constructed. Andrew Nelson and colleagues in their excellent review paper point out the important distinction between simulate-then-transfer-to-real, and embodied evolution in which the whole process takes place in the real world - in real-time and real-space.
In part two of the talk I outlined two approaches to embodied evolution. The first I call an engineering approach, in which the process is completely embodied but takes place in a kind of evolution factory; this approach needs a significant automated infrastructure: instead of an manufactory we need an evofactory. The second approach I characterise as an artificial life approach. Here there is no infrastructure. Instead 'smart matter' somehow mates then replicates offspring over multiple generations in a process much more analogous to biological evolution. This was one of the ambitious aims of the Symbrion project which, sadly, met with only limited success. Trying to make mechanical robots behave like evolving smart matter is really tough.
Part three concluded by outlining a number of significant challenges to evolving real robot bodies. First I reflect on the huge challenge of evolving complexity. To date we've only evolved very simple robots with very simple behaviours, or co-evolved simple brain/body combinations. I'm convinced that evolving robots of greater (and useful) complexity requires a new approach. We will, I think, need to understand how to co-evolve robots and their ecosystems*. Second I touch upon a related challenge: genotype-phenotype mapping. Here I refer to Pfeifer and Bongard's scalable complexity principle - the powerful idea that we shouldn't evolve robots directly, but instead the developmental process that will lead to the robot, i.e. artificial evo-devo. Finally I raise the often overlooked challenge of the energy cost of artificial evolution.
But the biggest challenge remains essentially what it was 20 years ago: to fully realise the artificial evolution of real robots.
Some of the work of this talk is set out in forthcoming paper: AFT Winfield and J Timmis, Evolvable Robot Hardware, in Evolvable Hardware, eds M Trefzer and A Tyrrell, Springer, in press.
*I touch upon this in the final para of my paper on the energy cost of evolution here.