Vergence control with a neuromorphic iCub

Vergence control and tracking allow a robot to maintain an accurate estimate of a dynamic object three dimensions, improving depth estimation at the fixation point. Brain-inspired implementations of vergence control are based on models of complex binocular cells of the visual cortex sensitive to disparity. The energy of cells activation provides a disparity-related signal that can be reliably used for vergence control. We implemented such a model on the neuromorphic iCub, equipped with a pair of brain inspired vision sensors. Such sensors provide low-latency, compressed and high temporal resolution visual information related to changes in the scene. We demonstrate the feasibility of a fully neuromorphic system for vergence control and show that this implementation works in real-time, providing fast and accurate control for a moving stimulus up to 2 Hz, sensibly decreasing the latency associated to frame-based cameras. Additionally, thanks to the high dynamic range of the sensor, the control shows the same accuracy under very different illumination.