  Nonlinear model predictive control (NMPC) is typically restricted to short,
finite horizons to limit the computational burden of online optimization. This
makes a global planner necessary to avoid local minima when using NMPC for
navigation in complex environments. For this reason, the performance of NMPC
approaches are often limited by that of the global planner. While control
policies trained with reinforcement learning (RL) can theoretically learn to
avoid such local minima, they are usually unable to guarantee enforcement of
general state constraints. In this paper, we augment a sampling-based
stochastic NMPC (SNMPC) approach with an RL trained perception-informed value
function. This allows the system to avoid observable local minima in the
environment by reasoning about perception information beyond the finite
planning horizon. By using Probably Approximately Correct NMPC (PAC-NMPC) as
our base controller, we are also able to generate statistical guarantees of
performance and safety. We demonstrate our approach in simulation and on
hardware using a 1/10th scale rally car with lidar.
