Staged Competence Learning in Developmental Robotics

Developmental psychology has long recognized the presence of stages in human cognitive development, although the underlying causes and processes are still an open question and subject to much debate. This article draws inspiration from psychology and describes an approach towards developmental growth for robotics that utilizes natural constraints in a general learning mechanism. The method, summarized as Lift-Constraint, Act, Saturate (LCAS), is applicable to all levels of control and behavior, and can be implemented in any robotic configuration. An implementation based on sensory-motor learning in early infancy is described and the results from experiments are presented and discussed.