No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation

Abstract:Test-time scaling offers a promising method to improve the inference performance of Vision-Language Models (VLMs) without additional training. Existing approaches to vision-language navigation (VLN) for Unmanned Aerial Vehicle (UAV) typically relies on a single inference pass, which can falter in complex environments by producing suboptimal or unsafe trajectories. In this paper, we explore a simple and effective approach to apply test-time scaling to VLN for UAV. We enhance navigation reasoning through an iterative refinement process that requires no extra model training, guiding the model to re-evaluate its initial navigation plan for better accuracy and safety. Our method first prompts the model to generate multiple parallel candidates and then performs a self-correction step, achieving deeper and more robust planning without changing the underlying model. To further strengthen decision-making, we design a multi-criteria scoring function to evaluate the refined candidates based on safety, goal alignment, and forward-progress. This simple yet powerful combination enables a frozen UAV navigation VLMs to self-correct and generate more accurate and reliable flight plans, achieving SOTA performance in this task.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2607.19288 [cs.CV]

(or arXiv:2607.19288v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.19288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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