A comparison of learning strategies for biologically constrained development of gaze control on an iCub robot

Gaze control requires the coordination of movements of both eyes and head to fixate on a target. We present a biologically constrained architecture for gaze control and show how the relationships between the coupled sensorimotor systems can be learnt autonomously from scratch, allowing for adaptation as the system grows or changes. Infant studies suggest developmental learning strategies, which can be applied to sensorimotor learning in humanoid robots. We examine two strategies (sequential and synchronous) for the learning of eye and head coupled mappings, and give results from implementations on an iCub robot. The results show that the developmental approach can give fast, cumulative, online learning of coupled sensorimotor systems