  Geometric methods for solving open-world off-road navigation tasks, by
learning occupancy and metric maps, provide good generalization but can be
brittle in outdoor environments that violate their assumptions (e.g., tall
grass). Learning-based methods can directly learn collision-free behavior from
raw observations, but are difficult to integrate with standard geometry-based
pipelines. This creates an unfortunate conflict -- either use learning and lose
out on well-understood geometric navigational components, or do not use it, in
favor of extensively hand-tuned geometry-based cost maps. In this work, we
reject this dichotomy by designing the learning and non-learning-based
components in a way such that they can be effectively combined in a
self-supervised manner. Both components contribute to a planning criterion: the
learned component contributes predicted traversability as rewards, while the
geometric component contributes obstacle cost information. We instantiate and
comparatively evaluate our system in both in-distribution and
out-of-distribution environments, showing that this approach inherits
complementary gains from the learned and geometric components and significantly
outperforms either of them. Videos of our results are hosted at
https://sites.google.com/view/hybrid-imitative-planning
