A Developmental Framework for Cumulative Learning Robots

• Published in: Computational and Robotic Models of the Hierarchical Organization of Behavior pp. 177 - 212
• Main Authors: Hülse, Martin, Lee, Mark, Law, James
• Format: Reference Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2013
• Subjects: Field Size; Humanoid Robot; Intrinsic Motivation; Proprioceptive Sensor; Robotics industry; Stimulus Point
• ISBN: 364239874X; 9783642398742
• DOI: 10.1007/978-3-642-39875-9_9

Developmental psychology is the study of human cognitive growth. However there exists a huge gap between the psychologist’s theories and knowledge of behaviour and our ability to implement developmental processes in autonomous agents. In this chapter we describe an approach towards developmental growth for robotics that utilises natural constraints in a general learning mechanism. The method, summarised as Lift-Constraint, Act, Saturate (LCAS), is described and illustrated with results from experiments. We discuss how this approach is grounded in the topics of sensory-motor abstraction, intrinsic motivation (as novelty), and staged learning, and our belief that robotics can learn much from infant psychology.