Sounds like an interesting project. I’d like to help if I can.
I’ve been working on a proof of concept app for doing stereo vision saccades for a while now. My objective has been to see if there is perhaps some natural architecture choice which would lead to the eyes saccading together. Another theory that I wanted to test was that the system would learn to saccade towards areas in the input that were producing unexpected behavior. In other words, they would tend to ignore stationary and simple movements in favor of focusing on input areas that were behaving unpredictably - motor output driven (or at least influenced) by bursting columns.
The input consists of a pair of 2D Cartesian grids of cells acting as retinas. I’ve also experimented with a radial density function to get more of a foveated effect. These inputs are then tied to hidden layers before being output to a pair of motor control layers. The motor control layers are also 2D Cartesian grids. I am currently interpreting the output layer as a weighted sum of the nodal positions to find the geometric center of activation for each layer. I then use the offsets from the origin to update the orientation for each eye - sort of like a joystick push. Perhaps you could use a similar mechanic to drive the movement of your sensor.