While Vision-Language-Action (VLA) models have achieved remarkable success in ground-based embodied intelligence, their application to Aerial Manipulation Systems (AMS) remains a largely unexplored frontier. The inherent characteristics of AMS, including floating-base dynamics, strong coupling between the UAV and the manipulator, and the multi-step, long-horizon nature of operational tasks, pose severe challenges to existing VLA paradigms designed for static or 2D mobile bases. To bridge this gap, we propose \textbf{AIR-VLA}, the first VLA benchmark specifically tailored for aerial manipulation. We construct a physics-based simulation environment and release a high-quality multimodal dataset comprising 3000 manually teleoperated demonstrations, covering base manipulation, object \& spatial understanding, semantic reasoning, and long-horizon planning. Leveraging this platform, we systematically evaluate mainstream VLA models and state-of-the-art VLM models. Our experiments not only validate the feasibility of transferring VLA paradigms to aerial systems but also, through multi-dimensional metrics tailored to aerial tasks, reveal the capabilities and boundaries of current models regarding UAV mobility, manipulator control, and high-level planning. \textbf{AIR-VLA} establishes a standardized testbed and data foundation for future research in general-purpose aerial robotics. The resource of AIR-VLA will be available at https://github.com/SpencerSon2001/AIR-VLA.

翻译：尽管视觉-语言-动作模型在基于地面的具身智能领域取得了显著成功，但其在空中操作系统中的应用仍是一个尚未充分探索的前沿领域。AMS固有的特性——包括浮基动力学、无人机与机械臂之间的强耦合性，以及操作任务的多步骤、长时域特性——对现有针对静态或二维移动基座设计的VLA范式构成了严峻挑战。为弥合这一差距，我们提出了首个专门为空中操作定制的VLA基准测试平台\textbf{AIR-VLA}。我们构建了基于物理的仿真环境，并发布了包含3000条人工遥操作演示的高质量多模态数据集，涵盖基座操控、物体与空间理解、语义推理及长时域规划。借助该平台，我们系统评估了主流VLA模型与最先进的VLM模型。实验不仅验证了将VLA范式迁移至空中系统的可行性，更通过针对空中任务定制的多维度指标，揭示了当前模型在无人机机动性、机械臂控制及高层规划方面的能力与局限。\textbf{AIR-VLA}为通用空中机器人的未来研究建立了标准化测试平台与数据基础。相关资源将通过https://github.com/SpencerSon2001/AIR-VLA公开。