Classification as Sensory-Motor Coordination: A Case Study on Autonomous Agents

In psychology classification is studied as a separate cognitive capacity. In the field of autonomous agents the robots are equipped with perceptual mechanisms for classifying objects in the environment, either by preprogramming or by some sorts of learning mechanisms. One of the well-known hard and fundamental problems is the one of perceptual aliasing, i.e. that the sensory stimulation caused by one and the same object varies enormously depending on distance from object, orientation, lighting conditions, etc. Efforts to solve this problem, say in classical computer vision, have only had limited success. In this paper we argue that classification cannot be viewed as a separate perceptual capacity of an agent but should be seen as a sensory-motor coordination which comes about through a self-organizing process. This implies that the whole organism is involved, not only sensors and neural circuitry. In this perspective, “action selection” becomes an integral part of classification. These ideas are illustrated with a case study of a robot that learns to distinguish between graspable and non-graspable pegs