A Demonstration of the Efficiency of Developmental Learning

Abstract

Previous research has suggested that developmental learning can make the learning of advanced sensorimotor and cognitive skills possible. In this paper, we demonstrate that developmental learning based on skill progression is also more efficient than traditional divide-and-conquer methods. Using a model based on the skills of reaching and pointing to visual targets, we demonstrate an implementation for a humanoid robot that is more efficient at learning joint attention skills than other published methods. This efficiency results from (1) a structured set of learning tasks that progresses from low-dimensional to high-dimensional problems and (2) a greater exploitation of the learning environment that does not follow from the completely task-based decomposition that divide-and-conquer provides.