Incremental Evolution of Robot Controllers for a Highly Integrated Task

In this paper we apply incremental evolution for automatic synthesis of neural network controllers for a group of physically connected mobile robots called s-bots. The robots should be able to safely and cooperatively perform phototaxis in an arena containing holes. We experiment with two approaches to incremental evolution, namely behavioral decomposition and environmental complexity increase. Our results are compared with results obtained in a previous study where several non-incremental evolutionary algorithms were tested and in which the evolved controllers were shown to transfer successfully to real robots. Surprisingly, none of the incremental evolutionary strategies performs any better than the non-incremental approach. We discuss the main reasons for this and why it can be difficult to apply incremental evolution successfully in highly integrated tasks.