We develop a bio-inspired controller for an active stereo vision system based on the Hering's law. We extend a model already proposed in literature in two ways. Firstly we evaluate the performance of the controller, inspecting its capability to foveate a generic feature in the 3D space, and the robustness respect to the initial angular configuration of the stereo system. Secondly we introduce the redundant component of the neck. Using a classical learning method we tune the controller to adapt to the controlled system. We investigate how the redundancy is solved by the learned controller, and show that the performance increases and the controlled stereo system generates human-like trajectories.