  The process of designing costmaps for off-road driving tasks is often a
challenging and engineering-intensive task. Recent work in costmap design for
off-road driving focuses on training deep neural networks to predict costmaps
from sensory observations using corpora of expert driving data. However, such
approaches are generally subject to over-confident mispredictions and are
rarely evaluated in-the-loop on physical hardware. We present an inverse
reinforcement learning-based method of efficiently training deep cost functions
that are uncertainty-aware. We do so by leveraging recent advances in highly
parallel model-predictive control and robotic risk estimation. In addition to
demonstrating improvement at reproducing expert trajectories, we also evaluate
the efficacy of these methods in challenging off-road navigation scenarios. We
observe that our method significantly outperforms a geometric baseline,
resulting in 44% improvement in expert path reconstruction and 57% fewer
interventions in practice. We also observe that varying the risk tolerance of
the vehicle results in qualitatively different navigation behaviors, especially
with respect to higher-risk scenarios such as slopes and tall grass.
