Active eye movements can be exploited to learn the oculomotor command that allows a humanoid head to gaze at a visual target. In the field of adaptive robotics this transformation is commonly encoded by means of neural networks that are trained using ballistic eye movements. In this work, we encode the visuo-oculomotor transformation using radial basis function networks and we derive the equations to use such a transformation in visual servo control tasks. We study how the choice of the input (monocular vs binocular) influences the execution of ballistic and visually-guided eye movements. Analytic analysis and experimental results on a simulated model of the robot show that the proposed framework can be employed to execute both kind of movements.
展开▼
