Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectification

As embodied AI transitions to real-world deployment, thesuccess of the Vision-and-Language Navigation (VLN) task tends to evolvefrom mere reachability to social compliance. However, current agentssuffer from a “Goal-driven trap”, prioritizing physical geometry (“can Igo?”) over semantic rules (“may I go?”), frequently overlooking subtleregulatory constraints. To bridge this gap, we establish Rule-VLN, the firstlarge-scale urban benchmark for rule-compliant navigation. Spanning amassive 29k-node environment, it injects 177 diverse regulatory categoriesinto 8k constrained nodes across four curriculum levels, challenging agentswith fine-grained visual and behavioral constraints. We further proposethe Semantic Navigation Rectification Module (SNRM), a universal, zero-shot module designed to equip pre-trained agents with safety awareness.SNRM integrates a coarse-to-fine visual perception VLM framework withan epistemic mental map for dynamic detour planning. Experimentsdemonstrate that while Rule-VLN challenges state-of-the-art models,SNRM significantly restores navigation capabilities, reducing CVR by19.26% and boosting TC by 5.97%. The project page is available athttps://jeffry-wen.github.io/Rule-VLN/.