Environment-specific novelty detection

Authors:
Stephen Marsland;Ulrich Nehmzow;Jonathan Shapiro
Affiliations:
Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK;Department of Computer Science, The University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK;Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK
Venue:
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Year:
2002

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Abstract

Novelty detection, recognising features that differ from those that are normally seen, is a potentially useful ability for a mobile robot. Once a robot can detect those features that are novel the amount of learning that has to be done can be reduced (as only new things need to be learnt), the attention of the robot can be focused onto the new features, and the robot can be used as an inspection agent.However, features that are novel in one place could be completely normal elsewhere - for example, tables and chairs are usually seen in offices, but very rarely seen in corridors. This paper suggests a method by which a set of novelty filters can be trained for different environments and the correct filter autonomously selected for the environment that the robot is currently travelling in. The method can also extend itself, so that further environments that are seen by the robot can be added without any retraining.