  Robotic vision introduces requirements for real-time processing of
fast-varying, noisy information in a continuously changing environment. In a
real-world environment, convenient assumptions, such as static camera systems
and deep learning algorithms devouring high volumes of ideally slightly-varying
data are hard to survive. Leveraging on recent studies on the neural connectome
associated with eye movements, we designed a neuromorphic oculomotor controller
and placed it at the heart of our in-house biomimetic robotic head prototype.
The controller is unique in the sense that (1) all data are encoded and
processed by a spiking neural network (SNN), and (2) by mimicking the
associated brain areas' topology, the SNN is biologically interpretable and
requires no training to operate. Here, we report the robot's target tracking
ability, demonstrate that its eye kinematics are similar to those reported in
human eye studies and show that a biologically-constrained learning, although
not required for the SNN's function, can be used to further refine its
performance. This work aligns with our ongoing effort to develop
energy-efficient neuromorphic SNNs and harness their emerging intelligence to
control biomimetic robots with versatility and robustness.
