Abstract. Genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour. The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved sensor configuration.

Abstract:Designing robots and robot controllers is a highly complex and often expensive task. However, genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. We show that, even with limited computational resources, genetic programming is able to evolve efficient robot controllers for corridor following in a simulation environment. Therefore, a mixed and gradual form of layered learning is used, resulting in very robust and efficient controllers. Furthermore, the controller is successfully applied to real environments as well.