  To navigate complex environments, robots must increasingly use
high-dimensional visual feedback (e.g. images) for control. However, relying on
high-dimensional image data to make control decisions raises important
questions; particularly, how might we prove the safety of a visual-feedback
controller? Control barrier functions (CBFs) are powerful tools for certifying
the safety of feedback controllers in the state-feedback setting, but CBFs have
traditionally been poorly-suited to visual feedback control due to the need to
predict future observations in order to evaluate the barrier function. In this
work, we solve this issue by leveraging recent advances in neural radiance
fields (NeRFs), which learn implicit representations of 3D scenes and can
render images from previously-unseen camera perspectives, to provide
single-step visual foresight for a CBF-based controller. This novel combination
is able to filter out unsafe actions and intervene to preserve safety. We
demonstrate the effect of our controller in real-time simulation experiments
where it successfully prevents the robot from taking dangerous actions.
