指示を超えてナビゲート：障害物環境における視覚と言語によるナビゲーション
Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments
視覚と言語によるナビゲーション（VLN）において、予期せぬ障害物による指示と現実の不一致に対応するため、R2R-UNOデータセットを構築し、適応的にナビゲートするObVLN手法を提案した。
著者: Haodong Hong, Sen Wang, Zi Huang, Qi Wu, Jiajun Liu
分類: cs.RO, cs.CL, cs.CV
原文アブストラクト
Real-world navigation often involves dealing with unexpected obstructions such as closed doors, moved objects, and unpredictable entities. However, mainstream Vision-and-Language Navigation (VLN) tasks typically assume instructions perfectly align with the fixed and predefined navigation graphs without any obstructions. This assumption overlooks potential discrepancies in actual navigation graphs and given instructions, which can cause major failures for both indoor and outdoor agents. To address this issue, we integrate diverse obstructions into the R2R dataset by modifying both the navigation graphs and visual observations, introducing an innovative dataset and task, R2R with UNexpected Obstructions (R2R-UNO). R2R-UNO contains various types and numbers of path obstructions to generate instruction-reality mismatches for VLN research. Experiments on R2R-UNO reveal that state-of-the-art VLN methods inevitably encounter significant challenges when facing such mismatches, indicating that they rigidly follow instructions rather than navigate adaptively. Therefore, we propose a novel method called ObVLN (Obstructed VLN), which includes a curriculum training strategy and virtual graph construction to help agents effectively adapt to obstructed environments. Empirical results show that ObVLN not only maintains robust performance in unobstructed scenarios but also achieves a substantial performance advantage with unexpected obstructions.
