AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation
Vision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment. While state-of-the-art methods leverage the reasoning capabilities of Vision-Language Models (VLMs) for end-to-end action prediction, they often lack an explicit and explainable understanding of the relationships between the agent, the instruction, and the scene. Conversely, explicitly building a scene map for heuristic planning is intuitively appealing but relies on additional 3D sensors and hinders large-scale vision-language pre-training. To bridge this gap, we propose AwareVLN, a novel framework that equips the navigation model with a self-aware reaso...
| Authors | Wenxuan Guo, Xiuwei Xu, Yichen Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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