Detecting Novel Features of an Environment Using Habituation

Abstract

In this paper a novelty filter is introduced which allows a robot operating in an unstructured environment to produce a self-organised model of its surroundings and to detect deviations from the learned model. The environment is perceived using the robot's 16 sonar sensors. The algorithm produces a novelty measure for each sensor scan relative to the model it has learned. This means that it highlights stimuli which have not been previously experienced. The novelty filter proposed uses a model of habituation. Habituation is a decrement in behavioural response when a stimulus is presented repeatedly. Robot experiments are presented which demonstrate the reliable operation of the filter in a number of environments.