Two Examples of Active Categorisation Processes Distributed Over Time

Active perception refers to a theoretical approach grounded on the idea that perception is an active process in which the actions performed by the agent play a constitutive role. In this paper we present two different scenarios in which we test active perception principles using an evolutionary robotics approach. In the first experiment, a robotic arm equipped with coarse-grained tactile sensors is required to perceptually categorize spherical and ellipsoid objects. In the second experiment, an active vision system has to distinguish between five different kinds of images of different sizes. In both situations the best individuals develop a close to optimal ability to discriminate different objects/images as well as an excellent ability to generalize their skills in new circumstances. Analyses of evolved behaviours show that agents are able to solve their tasks by actively selecting relevant information and by integrating these information over time.