Map2Route：基于语义地图的组合式语言引导路线规划基准

中文摘要 AI 辅助

我们引入了Map2Route，这是一个人工策划的基准，用于在预构建的语义地图上进行组合式语言引导的路线规划。Map2Route包含40个场景中的1000个片段，其中指令使用关系型、比较型和嵌套型描述来识别与路线相关的对象和区域，同时指定有序的必经区域、必须避开的要求、五类软偏好以及空间和路线阶段范围，这些现有工作仅部分测试过。伴随Map2Route，我们提出了Grounding2Route，它将可执行的代码作为基础与验证引导的修复和范围感知的细化相结合。在七个代表性改编基线上，Grounding2Route在所有指标上大幅优于现有方法。尽管取得了这些进展，与人类演示相比仍有显著差距，这突显了Map2Route的难度以及未来进步的广阔空间。额外的定性结果和资源可在提供的网址上获取。

英文摘要

We introduce Map2Route, a human-curated benchmark for compositional language-grounded route planning over pre-built semantic maps. Map2Route contains 1,000 episodes across 40 scenes, where instructions use relational, comparative, and nested descriptions to identify route-relevant objects and regions, while specifying ordered must-pass regions, must-avoid requirements, five categories of soft preferences, and spatial and route-stage scopes, which is partially tested by existing works. Alongside Map2Route, we propose Grounding2Route, which combines executable code-as-grounding with verification-guided repair and scope-aware planning.Across seven representative adapted baselines, Grounding2Route substantially outperforms existing methods in all metrics. Despite these gains, a substantial gap to human demonstrations remains, highlighting the difficulty of Map2Route and the considerable headroom for future progress. Additional qualitative results and resources are available on https://anonymous.4open.science/w/Map2Route-F05F/.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）
• Nanyang Technological University（南洋理工大学）

机构由 AI 辅助整理，请以论文原文为准。