AerialVLA: A Vision-Language-Action Model for UAV Navigation via Minimalist End-to-End Control

TL;DR

AerialVLA introduces a minimalist, end-to-end vision-language-action framework for UAV navigation that eliminates reliance on dense guidance and object detectors, achieving state-of-the-art results and superior generalization.

Contribution

The paper presents a novel end-to-end UAV navigation model that uses a simplified perception strategy and fuzzy directional prompts, enhancing autonomy and transferability.

Findings

Achieves state-of-the-art performance on TravelUAV benchmark.

Nearly three times higher success rate in unseen environments.

Demonstrates robustness with minimalist design and intrinsic control signals.

Abstract

Vision-Language Navigation (VLN) for Unmanned Aerial Vehicles (UAVs) demands complex visual interpretation and continuous control in dynamic 3D environments. Existing hierarchical approaches rely on dense oracle guidance or auxiliary object detectors, creating semantic gaps and limiting genuine autonomy. We propose AerialVLA, a minimalist end-to-end Vision-Language-Action framework mapping raw visual observations and fuzzy linguistic instructions directly to continuous physical control signals. First, we introduce a streamlined dual-view perception strategy that reduces visual redundancy while preserving essential cues for forward navigation and precise grounding, which additionally facilitates future simulation-to-reality transfer. To reclaim genuine autonomy, we deploy a fuzzy directional prompting mechanism derived solely from onboard sensors, completely eliminating the dependency on dense oracle guidance. Ultimately, we formulate a unified control space that integrates continuous 3-Degree-of-Freedom (3-DoF) kinematic commands with an intrinsic landing signal, freeing the agent from external object detectors for precision landing. Extensive experiments on the TravelUAV benchmark demonstrate that AerialVLA achieves state-of-the-art performance in seen environments. Furthermore, it exhibits superior generalization in unseen scenarios by achieving nearly three times the success rate of leading baselines, validating that a minimalist, autonomy-centric paradigm captures more robust visual-motor representations than complex modular systems.