Evolutionary Approaches to Neural Control of Rolling, Walking, Swimming and Flying Animats or Robots
Jean-Arcady Meyer, Stéphane Doncieux, David Filliat, Agnès Guillot · Studies in fuzziness and soft computing · 2003
This article describes past and current research efforts in evolutionary robotics that have been carried out at the AnimatLab, Paris. Such approaches entail using an artificial selection process to automatically generate developmental programs for neural networks that control rolling, walking, swimming and flying animats or robots. Basically, they complement the underlying evolutionary process with a developmental procedure — in order to hopefully reduce the size of the genotypic space that is explored — and they occasionally call on an incremental approach, in order to capitalize upon solutions to simpler problems so as to devise solutions to more complex problems. This article successively outlines the historical background of our research, the evolutionary paradigm on which it relies, and the various results obtained so far. It also discusses the potentialities and limitations of the approach and indicates directions for future work. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.