No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation
2026-07-21 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionRobotics
AI summaryⓘ
The authors found a way to improve how drones use vision-language models to find their way without needing extra training. They made the model check its own navigation plan multiple times and pick the safest and most accurate option using a scoring system. This approach helps the drone plan better routes in tricky environments by refining its choices on the spot. Their method works well with existing models and achieves top results in UAV navigation tasks.
Vision-Language ModelsUnmanned Aerial VehicleVision-Language NavigationTest-time scalingInferenceIterative refinementSelf-correctionMulti-criteria scoringNavigation planningSafety in UAV
Authors
Feinan Cheng, Dongliang Xu, Wenli Nong, Zhiheng Zhang, Ang Liu, Tianyu Wang, Yue Yao
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.