VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910145502511104 |
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| author | Su, Hung-Ting Wang, Ting-Jun Yeh, Jia-Fong Sun, Min Hsu, Winston H. |
| author_facet | Su, Hung-Ting Wang, Ting-Jun Yeh, Jia-Fong Sun, Min Hsu, Winston H. |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10533 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions Su, Hung-Ting Wang, Ting-Jun Yeh, Jia-Fong Sun, Min Hsu, Winston H. Robotics Computation and Language Computer Vision and Pattern Recognition 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/. |
| title | VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions |
| topic | Robotics Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.10533 |