用合成数据训练，让机器人导航成本图既能泛化新地形又能快速适应任务需求。
Terrain Costmap Generation via Scaled Preference Conditioning
- 通过用户指定的偏好条件，动态调整地形成本分布。
- 在七种环境中的五种里，路径规划的损失最低，优于现有方法。
- 适合需要快速适配不同任务的野外自主导航系统。
在非结构化环境中实现自主机器人导航，需生成高质量的地形成本图，既能在多种地形间良好泛化，又能在测试时快速适应任务特定的成本需求。现有方法或能实现快速测试时成本调整（如语义分割），或能泛化到新地形类型（如表示学习），但无法兼顾两者。本文提出一种名为SPACER的新方法，利用合成数据训练，使成本图能有效泛化至新地形，并通过用户指定的缩放偏好上下文实现快速测试时成本调整。基于大规模航拍地图的实验证明，SPACER在七种环境中的五种里，全局路径规划的测量后悔值最低，显著优于其他方法。
原文摘要 · Abstract (English)
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.
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