Keywords: imitation learning, robotics, navigation, motion planning
TL;DR: Combining geometric costmaps and learned models improves long-range navigation in offroad environments.
Abstract: Mobile robots tasked with reaching user-specified goals in open-world outdoor environments must contend with numerous challenges, including complex perception and unexpected obstacles and terrains. Prior work has addressed such problems with geometric methods that reconstruct obstacles, as well as learning-based methods. While geometric methods provide good generalization, they can be brittle in outdoor environments that violate their assumptions (e.g., tall grass). On the other hand, learning-based methods can learn to directly select collision-free paths from raw observations, but are difficult to integrate with standard geometry-based pipelines. This creates an unfortunate ``either-or" dichotomy -- 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. The main idea of our approach is reject this dichotomy by designing the learning and non-learning-based components in a way such that they can be easily and effectively combined and created without labeling any data. 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 a high-fidelity simulator. We show that this approach inherits complementary gains from both components: the learning-based component enables the system to quickly adapt its behavior, and the geometric component often prevents the system from making catastrophic errors.