A Robot Implementation of a Biologically Inspired Method for Novelty Detection

Abstract

This work examines the ability of a biologically inspired novelty detection model to learn and detect changes in the environment of a mobile robot. The novelty detection model used was inspired by recent neurological findings of novelty neurons in monkeys' perirhinal cortices. Experiments examine the difference required between stimuli before the novelty detection model recognises them as novel and the ability of the model to learn its environment on-line. The novelty detection model examined in this paper is based on calculating the energy of a Hopfield network. It appears to be potentially useful for on-line learning on mobile robots as it can reliably learn from a single presentation of a novel stimuli. A qualitative comparison is made to an alternative model that also carries out novelty detection on a mobile robot.