TARIC: Memory-Augmented Traversability-Aware Outdoor VLN under Interrupted Semantic Cues 文章

ArXiv CS.AI2026-06-01NEWSen作者: Tianle Zeng, Hanjing Ye, Jianwei Peng, Jingwen Yu, Hanxuan Chen, Hong Zhang

摘要

arXiv:2605.31121v1 Announce Type: cross Abstract: Outdoor vision-language navigation (VLN) in long-range, open-world environments is frequently disrupted by semantic-cue interruptions, where informative goal cues become sparse, occluded, or leave the field of view. Once such cues disappear, agents enter a cue-free phase and often degrade into backtracking, oscillatory headings, or aimless exploration. While memory-based methods attempt to bridge these gaps, they often fail under traversability-driven detours: the remembered cue direction may be infeasible, forcing detours that prolong cue-free phases and gradually render robot-centric cues stale and implicit histories blurred. This makes traversability a stability condition for maintaining goal-directed guidance, rather than merely a local safety concern.

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