Low-Altitude Wireless Networks (LAWNs), composed of Unmanned Aerial Vehicles (UAVs) and other aerial platforms, provide integrated perception, communication, and computation services in low-altitude airspace. However, deploying large generative models in this domain faces three major challenges: 1) Limited embodied action mapping; 2) Inadequate physical environment modeling; 3) Insufficient closed-loop optimization. To address these challenges, this study proposes an Embodied Agentic UAV framework. Centered on a Vision-Language-Action (VLA) model as the execution core, the framework establishes an end-to-end embodied decision-making pipeline from multimodal environmental perception to continuous control generation. In addition, a World Model (WM) is introduced to capture the coupling between UAV actions and environmental state evolution, thereby supporting environment prediction, policy verification, and dynamic optimization. Furthermore, memory and reflection mechanisms are incorporated to form an adaptive closed-loop optimization paradigm of decision, execution, evaluation, and update, thereby enhancing the system's autonomous decision-making capability and continual evolution ability in complex dynamic environments. Experimental results validate its effectiveness in enabling robust, predictive, and sustainable autonomous control in LAWNs.

翻译：低空无线网络（LAWNs）由无人机（UAVs）及其他空中平台构成，在低空空域提供集成感知、通信与计算服务。然而，在该领域部署大规模生成式模型面临三大挑战：1）具身动作映射能力有限；2）物理环境建模不完善；3）闭环优化不足。针对上述挑战，本研究提出了一种具身智能体无人机框架。该框架以视觉-语言-动作（VLA）模型为执行核心，构建了从多模态环境感知到连续控制生成的端到端具身决策流水线。此外，引入世界模型（WM）以捕捉无人机动作与环境状态演化之间的耦合关系，从而支持环境预测、策略验证与动态优化。进一步融合记忆与反思机制，形成“决策-执行-评估-更新”的自适应闭环优化范式，增强系统在复杂动态环境中的自主决策能力与持续演进能力。实验结果验证了该方法在实现LAWNs中鲁棒、可预测且可持续的自主控制方面的有效性。