Map2Route: Benchmarking Compositional Language-Grounded Route Planning over Semantic Maps

2026-09-15

AI Summary

本文通过引入Map2Route基准和提出Grounding2Route方法，解决了基于语义地图的语言导向路径规划问题。

Abstract

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/.

Problem

• compositional language
• route planning
• semantic maps
• relational descriptions
• soft preferences

Innovation

• compositional language-grounded route planning
• semantic maps
• Grounding2Route
• verification-guided repair
• scope-aware planning