  In order to certify performance and safety, feedback control requires precise
characterization of sensor errors. In this paper, we provide guarantees on such
feedback systems when sensors are characterized by solving a supervised
learning problem. We show a uniform error bound on nonparametric kernel
regression under a dynamically-achievable dense sampling scheme. This allows
for a finite-time convergence rate on the sub-optimality of using the regressor
in closed-loop for waypoint tracking. We demonstrate our results in simulation
with simplified unmanned aerial vehicle and autonomous driving examples.
