Learning Robotic Hand-eye Coordination Through a Developmental Constraint Driven Approach

The skill of robotic hand-eye coordination not only helps robots to deal with real time environment, but also affects the fundamental framework of robotic cognition. A number of approaches have been developed in the literature for construction of the robotic hand-eye coordination. However, several important features within infant developmental procedure have not been introduced into such approaches. This paper proposes a new method for robotic hand-eye coordination by imitating the developmental progress of human infants. The work employs a brain-like neural network system inspired by infant brain structure to learn hand-eye coordination, and adopts a developmental mechanism from psychology to drive the robot. The entire learning procedure is driven by developmental constraint: The robot starts to act under fully constrained conditions, when the robot learning system becomes stable, a new constraint is assigned to the robot. After that, the robot needs to act with this new condition again. When all the contained conditions have been overcome, the robot is able to obtain hand-eye coordination ability. The work is supported by experimental evaluation, which shows that the new approach is able to drive the robot to learn autonomously, and make the robot also exhibit developmental progress similar to human infants.