In this article we describe how a population of evolving robots can autonomously develop forms of spatial representation which allow them to self-localize and to discriminate different locations of their environment by integrating sensory-motor information over time. The evolving robots also display a remarkable ability to generalize their skill in new environmental conditions that they have never experienced before. The analysis of the obtained results indicates that the evolved robots come up with simple and robust solutions that exploit quasi-periodic limit cycle dynamics emerging from the coupling between the robot/environmental dynamics and a robot's internal dynamics. More specifically, the variations of a robot's internal states are governed by transient dynamical processes originating from the fact that these internal states tend to slowly approximate fixed attractor points, corresponding to different types of sensory states that last for a limited time duration and alternate while the robot moves in the environment.
