Successful autonomous robot navigation in off-road domains requires the ability to generate high-quality terrain costmaps that are able to both generalize well over a wide variety of terrains and rapidly adapt relative costs at test time to meet mission-specific needs. Existing approaches for costmap generation allow for either rapid test-time adaptation of relative costs (e.g., semantic segmentation methods) or generalization to new terrain types (e.g., representation learning methods), but not both. In this work, we present scaled preference conditioned all-terrain costmap generation (SPACER), a novel approach for generating terrain costmaps that leverages synthetic data during training in order to generalize well to new terrains, and allows for rapid test-time adaptation of relative costs by conditioning on a user-specified scaled preference context. Using large-scale aerial maps, we provide empirical evidence that SPACER outperforms other approaches at generating costmaps for terrain navigation, with the lowest measured regret across varied preferences in five of seven environments for global path planning.

翻译：在越野环境中实现成功的自主机器人导航，需要具备生成高质量地形代价图的能力，这些代价图应能广泛适应多种地形，并能在测试时快速调整相对代价以满足特定任务需求。现有的代价图生成方法要么允许在测试时快速调整相对代价（例如语义分割方法），要么能泛化到新的地形类型（例如表示学习方法），但无法同时实现两者。在本工作中，我们提出了基于缩放偏好条件化的全地形代价图生成方法（SPACER），这是一种新颖的地形代价图生成方法。该方法在训练阶段利用合成数据以实现对新地形的良好泛化，并通过条件化于用户指定的缩放偏好上下文，实现测试时相对代价的快速调整。基于大规模航拍地图，我们提供的实证证据表明，在生成用于地形导航的代价图方面，SPACER优于其他方法，在七个环境中的五个里，其全局路径规划在不同偏好下测得的遗憾值最低。