Adaptive classification in autonomous agents

Abstract

One of the fundamental tasks facing autonomous robots is to reduce the many degrees of freedom of the input space by some sorts of classification mechanism. The sensory stimulation caused by one and the same object, for instance, varies enormously depending on lighting conditions, distance from object, orientation and so on. Efforts to solve this problem, say in classical computer vision, have only had limited success. In this paper a new approach towards classification in autonomous robots is proposed. It's cornerstone is the integration of the robots own actions into the classification process. More specifically, correlations through time-linked independent samples of sensory stimuli and of kinesthetic signals produced by self-motion of the system form the basis of the category learning. Thus, it is suggested that classification should not be seen as an isolated perceptual (sub-)system but rather as a sensory-motor coordination which comes about through a self-organizing process. The...