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

Vision-Language Navigation (VLN) for Unmanned AerialVehicles (UAVs) demands complex visual interpretation and continuouscontrol in dynamic 3D environments. Existing hierarchical approachesrely on dense oracle guidance or auxiliary object detectors, creating se-mantic gaps and limiting genuine autonomy. We propose AeroVLA, aminimalist end-to-end Vision-Language-Action framework mapping rawvisual observations and fuzzy linguistic instructions directly to contin-uous physical control signals. First, we introduce a streamlined dual-view perception strategy that reduces visual redundancy while preserv-ing essential cues for forward navigation and precise grounding, whichadditionally facilitates future simulation-to-reality transfer. To reclaimgenuine autonomy, we deploy a fuzzy directional prompting mechanismderived solely from onboard sensors, completely eliminating the depen-dency on dense oracle guidance. Ultimately, we formulate a unified con-trol space that integrates continuous 3-Degree-of-Freedom (3-DoF) kine-matic commands with an intrinsic landing signal, freeing the agent fromexternal object detectors for precision landing. Extensive experiments onthe TravelUAV benchmark demonstrate that AeroVLA achieves state-of-the-art performance in seen environments. Furthermore, it exhibitssuperior generalization in unseen scenarios by achieving nearly threetimes the success rate of leading baselines, validating that a minimal-ist, autonomy-centric paradigm captures more robust visual-motor rep-resentations than complex modular systems. Code is available at: https://github.com/XuPeng23/AeroVLA