Novelty detection as an intrinsic motivation for cumulative learning robots

Abstract

Novelty detection is an inherent part of intrinsic motivations and constitutes an important research issue for the effective and long-term operation of intelligent robots designed to learn, act and make decisions based on their cumulative knowledge and experience. Our approach to novelty detection is from the perspective that the robot ignores perceptions that are already known, but is able to identify anything different. This is achieved by developing biologically inspired novelty detectors based on habituation. Habituation is a type of non-associative learning used to describe the behavioural phenomenon of decreased responsiveness of a cognitive organism to a recently and frequently presented stimulus, and it has been observed in a number of biological organisms. This chapter first considers the relationship between intrinsic motivations and novelty detection and outlines some works on intrinsic motivations. It then presents a critical review of the methods of novelty detection published by the authors. A brief summary of some key recent surveys in the field is then provided. Finally, key open challenges that need to be considered in the design of novelty detection filters for cumulative learning tasks are discussed.