Hierarchical Perception– Decision Test-Time Adaptation for UAV Vision-Language Navigation

Unmanned aerial vehicle vision–language navigation (UAV-VLN) has attracted increasing attention, requiring an embodied agent to follow natural-language instructions by sequentially reasoning over visual observations in open 3D environments and executing navigation actions. Despite recent progress, more drastic viewpoint and scene variations in unseen environments can induce substantial train–test distribution shifts, manifested as cross-modal misalignment and feature drift at the perception stage, as well as biased action prediction at the decision stage. To address these issues, we propose Hierarchical Perception– Decision Test-Time Adaptation for UAV-VLN. At the perception stage, we propose a Memory-Augmented Cross-modal Perception Adaptation (MACPA) module. By constructing an environment semantic memory bank and introducing adaptive tokens, MACPA generates adaptive semantic guidance prompts to mitigate semantic drift. At the decision stage, we propose an Entropy-Gated Historical Decision Calibration (EHDC) module, which aggregates high-confidence historical action distributions based on the similarity between the current state and cached features, alleviating decision drift and enhancing the consistency and stability of predictions. Extensive experiments on the UAV-VLN benchmarks, OpenFly and OpenUAV, demonstrate that our method achieves better navigation performance and more stable decision behavior than state-of-the-art approaches in unseen environments.