Integration of brain-like computational structure and infant behaviorial pattern for robotic hand-eye coordination

Robotic hand-eye coordination plays an important role in dealing with real time environment; and the learning procedure of this skill affects the fundamental framework of robotic cognition. This paper introduces a novel developmental approach to hand-eye coordination in an autonomous robotic system. Existing work employs neural network models to map visual perception to hand. In the approach, a computational structure and a cross-modal link mechanism are applied to simulate brain cortices; and a movement pattern inspired by infant behaviors is designed to help robot learn to build its hand-eye coordination. This work is supported by experimental evaluation, which shows that the learning algorithm provides a fast and incremental learning of behavioral competence.