  In this work, we present a method for obtaining an implicit objective
function for vision-based navigation. The proposed methodology relies on
Imitation Learning, Model Predictive Control (MPC), and an interpretation
technique used in Deep Neural Networks. We use Imitation Learning as a means to
do Inverse Reinforcement Learning in order to create an approximate cost
function generator for a visual navigation challenge. The resulting cost
function, the costmap, is used in conjunction with MPC for real-time control
and outperforms other state-of-the-art costmap generators in novel
environments. The proposed process allows for simple training and robustness to
out-of-sample data. We apply our method to the task of vision-based autonomous
driving in multiple real and simulated environments and show its
generalizability.
