Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments

TL;DR

This paper introduces a hybrid planning approach combining learned traversability predictions with geometric obstacle costs, enabling robust off-road navigation in diverse environments.

Contribution

It presents a novel self-supervised method that effectively integrates learning-based and geometric planning components for off-road navigation.

Findings

Significantly outperforms purely geometric or learned methods in diverse environments.

Demonstrates robustness in both in-distribution and out-of-distribution scenarios.

Shows improved navigation success rates and safety.

Abstract

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