Understanding the mechanism mediating the change from inaccurate pre-reaching to accurate reaching in infants may confer advantage from both a robotic and biological research perspective. In this work, we present a biologically meaningful learning scheme applied to the coordination between reach and gaze within a robotic structure. The system is model-free and does not utilize a global reference system. The integration of reach and gaze emerges from the learned cross-modal mapping between reach and vision space as it occurs during the robot-environment interaction. The scheme showed high learning speed and plasticity compared with other approaches due to the low level of training data required. We discuss our findings with respect to biological plausibility and from an engineering perspective, with emphasis on autonomous learning as well as strategies for the selection of new training data.