Intrinsically motivated learning systems based on biologically-inspired novelty detection

• We proposed novelty detection as intrinsic motivation for autonomous learning.
• Novelty detection model is bio-inspired by the behavioural phenomenon of habituation.
• We demonstrated the effectiveness of the model in a real-world experiment.
• We showed how habituation is used as an intrinsic motivation for robot learning.

Intrinsic motivations play an important role in human learning, particularly in the early stages of childhood development, and ideas from this research field have influenced robotic learning and adaptability. In this paper we investigate one specific type of intrinsic motivation, that of novelty detection and we discuss the reasons that make it a powerful facility for continuous learning. We formulate and present one original type of biologically inspired novelty detection architecture and implement it on a robotic system engaged in a perceptual classification task. The results of real-world robot experiments we conducted show how this original architecture conforms to behavioural observations and demonstrate its effectiveness in terms of focusing the system’s attention in areas that are potential for effective learning.