让AI导航时学会识别不存在的目标，避免盲目寻找。
VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions
- 用大模型生成虚假目标指令，构建真实但错误的导航任务
- 新指标同时评估到达、探索和判断正确性，最高得分为0.32
- 提出双阶段方法，能主动探索并正确识别目标不存在
传统视觉语言导航（VLN）任务假设指令可行且目标存在，导致智能体无法应对虚假前提。本文提出VLN-NF基准，通过大模型重写指令并用视觉语言模型验证目标缺失，构建出看似合理却事实错误的导航任务。我们设计了REV-SPL指标，联合评估房间到达、探索覆盖率与决策正确性。针对该挑战，提出ROAM方法：第一阶段监督导航至目标房间，第二阶段利用大模型与视觉语言模型在室内探索，结合自由空间优先策略。实验显示，ROAM在所有对比方法中取得最优的REV-SPL（0.32），而基线模型常因不可靠指令过早终止或探索不足。
原文摘要 · Abstract (English)
Conventional Vision-and-Language Navigation (VLN) benchmarks assume instructions are feasible and the referenced target exists, leaving agents ill-equipped to handle false-premise goals. We introduce VLN-NF, a benchmark with false-premise instructions where the target is absent from the specified room and agents must navigate, gather evidence through in-room exploration, and explicitly output NOT-FOUND. VLN-NF is constructed via a scalable pipeline that rewrites VLN instructions using an LLM and verifies target absence with a VLM, producing plausible yet factually incorrect goals. We further propose REV-SPL to jointly evaluate room reaching, exploration coverage, and decision correctness. To address this challenge, we present ROAM, a two-stage hybrid that combines supervised room-level navigation with LLM/VLM-driven in-room exploration guided by a free-space clearance prior. ROAM achieves the best REV-SPL among compared methods, while baselines often under-explore and terminate prematurely under unreliable instructions. VLN-NF project page can be found at https://vln-nf.github.io/.
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