VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions
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....
| Authors | Hung-Ting Su, Ting-Jun Wang, Jia-Fong Yeh et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 April 2026 |
| Last updated at source | 19 April 2026 |
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