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
翻译:通过学习占用和测量地图,解决开放世界越野航行任务的几何方法,提供了良好的概括性,但在违反其假设的室外环境中(例如高草)可能会变得不易理解,学习方法可以直接从原始观测中学习无碰撞行为,但很难与标准的几何管道相结合。这造成了一个不幸的冲突 -- -- 要么利用学习和丢失在深陷的几何导航部件上,要么不使用这种系统,取而代之,取而代之的是广泛的手对地测量成本图。在这项工作中,我们拒绝这种二分法,办法是设计学习和非学习的部件,以便能够以自我监督的方式有效地将它们结合起来。这两个组成部分都有助于制定规划标准:学习的部件可以作为回报提供预测的可穿越性,而几何部分则提供障碍性信息。我们在分布和分配环境内对我们的系统进行即时和比较评价,表明这一方法可以继承从所学和几何计量的部件中取得的补充性收益,而且大大超出这些组成部分的形状。我们结果的视频是位于httphyblogimage/gomegrations。