Multi-objective evolution of robot neuro-controllers

This paper concerns a non-traditional evolutionary robotics approach to robot navigation. Navigation is presented as a problem of two conflicting objectives. The first concerns a classical "amalgamated" objective, which has been traditionally used to increase speed, move straight as possible, and at the same time avoid obstacles. The second objective is devised to simultaneously encourage a sequential acquisition of targets. To solve the presented problem a modification of the well known NSGAII algorithm has been performed. The proposed approach is tested using a simulation of a Khepera. The study sheds light on different aspects of the aforementioned problem and on the applicability of evolutionary multi-objective optimization to the simultaneous learning of a variety of controllers for deferent behaviors. Finally, based on this initial study, future work is suggested, which may allow to shift such multiobjective evolutionary studies from toy problems to more realistic situations.