  Recognising new or unusual features of an environment is an ability which is
potentially very useful to a robot. This paper demonstrates an algorithm which
achieves this task by learning an internal representation of `normality' from
sonar scans taken as a robot explores the environment. This model of the
environment is used to evaluate the novelty of each sonar scan presented to it
with relation to the model. Stimuli which have not been seen before, and
therefore have more novelty, are highlighted by the filter. The filter has the
ability to forget about features which have been learned, so that stimuli which
are seen only rarely recover their response over time. A number of robot
experiments are presented which demonstrate the operation of the filter.
