Gaze control requires the coordination of movements of both eyes and head to fixate on a target. Using our biologically constrained architecture for gaze control we 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 environmental constraints for the learning of eye and head coupled mappings, and give results from implementations on an iCub robot. The results show the impact of these constraints and how they can be overcome to benefit the development of fast, cumulative, on-line learning of coupled sensorimotor systems.