Sensory Flow Segmentation Using a Resource Allocating Vector Quantizer

摘要

We present a very simple unsupervised vector quantizer which extracts higher order concepts from time series generated from sensors on a mobile robot as it moves through an environment. The vector quantizer is constructive, i.e. it adds new model vectors, each one encoding a separate higher order concept, to account for any novel situation the robot encounters. The number of higher order concepts is determined dynamically, depending on the complexity of the sensed environment, without the need of any user intervention. We show how the vector quantizer elegantly handles many of the problems faced by an existing architecture by Nolfi and Tani, and note some directions for future work.