A Redescriptive Approach to Autonomous Perceptual Classification in Robotic Cognitive Architectures

Abstract

This paper is concerned with the problem of perceptual classification in the framework of life-long learning developmental cognitive architectures. Perceptual classification is the process by which autonomous entities organize their, usually continuous, perceptual streams into classes of perceptions that are relevant to the different contexts in which they find themselves. In particular, here we describe an approach based on context related generalization or categorization of perceptions in an autonomous manner within embodied systems. This approach involves the introduction of a new type of knowledge nuggets, Pnodes, within the long term memory structure of a cognitive architecture. Taking inspiration from the hippocampus-cortex relationships in real brains, P-nodes are initially described by means of a set of representative perceptual points which are subsequently generalized in a cortex like neural representation. This approach is tested in a series of experiments on a real robot.