Stop Wandering: Efficient Vision-Language Navigation via Metacognitive Reasoning
Training-free Vision-Language Navigation (VLN) agents powered by foundation models can follow instructions and explore 3D environments. However, existing approaches rely on greedy frontier selection and passive spatial memory, leading to inefficient behaviors such as local oscillation and redundant revisiting. We argue that this stems from a lack of metacognitive capabilities: the agent cannot monitor its exploration progress, diagnose strategy failures, or adapt accordingly. To address this, we propose MetaNav, a metacognitive navigation agent integrating spatial memory, history-aware planning, and reflective correction. Spatial memory builds a persistent 3D semantic map. History-aware p...
| Authors | Xueying Li, Feng Lyu, Hao Wu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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