Evolving robots able to self-localize in the environment: The importance of viewing cognition as the result of processes occurring at different time scales

In this paper we address the problem of synthesizing mobile robots able to solve problems in which they cannot merely react to sensory input, but have to maintain an internal state as well. More precisely we will show how autonomous robots synthesized through an evolutionary process can solve problems that necessarily require an ability to integrate sensory- motor information over time. By presenting the result of a set of experiments in which evolving robots are asked to navigate and self- localize in the environment, we will show that successful results can be achieved by providing evolving individuals with neural controllers with neurons that (a) vary their activity at different rates to detect regularities at different time scales in the sensory-motor flow, and (b) use thresholded activation functions to detect events extending over time.