发表机构
Institute of Automation, Chinese Academy of Sciences; Beihang University; Shanxi Datong University; Johns Hopkins University; University of Sanya; Wenzhou-Kean University; Macau University of Science and Technology; KOKONI 3D, Moxin Technology; University of Science and Technology of China（中国科学院自动化研究所; 北京航空航天大学; 山西大同大学; 约翰斯·霍普金斯大学; 三亚学院; 温州肯恩大学; 澳门科技大学; 魔芯科技KOKONI 3D; 中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出无人机具身智能范式，通过5+5框架系统综述其能力维度与架构，并指出驾驭物理-数字AI智能体是实现自适应持续演化无人机自主性的关键路径。
AI 中文摘要
无人机（UAVs）将具身智能扩展到连续的三维空间，其中感知、推理、物理具身和行动通过飞行与环境交互紧密耦合。近年来，基础模型、世界模型和AI智能体的进展正将无人机自主性从特定任务的感知与控制，转向能够解释人类意图、理解开放环境、推理物理后果，并在具身和飞行动力学约束下组织复杂行为的系统。我们将这一新兴范式定义为无人机具身智能（UAV EI），并将其与其系统实现——具身智能无人机（EI UAV）——区分开来。为提供该领域的统一视角，我们引入一个5+5框架，通过五个能力维度描述UAV EI，并通过五个架构层（涵盖物理具身、通用认知、具身技能、外部交互和系统驾驭）描述EI UAV。基于此框架，我们系统回顾了具身形态、具身感知、世界模型、具身规划、视觉-语言导航、具身操作和具身协作方面的最新进展。我们进一步指出长时程自主性、预测性物理推理、测试时技能获取和自主能力演化是迈向更通用空中具身智能的关键挑战。最后，我们认为通过将数字智能与物理感知、动力学、行动和反馈持续耦合，驾驭物理-数字AI智能体为自适应且持续演化的无人机自主性提供了一条系统级路径。项目资源可在我们的项目网站和GitHub仓库获取。
英文摘要
Unmanned aerial vehicles (UAVs) extend embodied intelligence into continuous three-dimensional space, where perception, reasoning, physical embodiment, and action are tightly coupled through flight and environmental interaction. Recent advances in foundation models, world models, and AI agents are shifting UAV autonomy from task-specific perception and control toward systems that can interpret human intent, understand open environments, reason about physical consequences, and organize complex behaviors under embodiment and flight-dynamic constraints. We characterize this emerging paradigm as UAV embodied intelligence (UAV EI) and distinguish it from its system realization, the embodied-intelligent UAV (EI UAV). To provide a unified view of the field, we introduce a 5+5 framework that describes UAV EI through five capability dimensions and EI UAVs through five architectural layers spanning physical embodiment, general cognition, embodied skills, external interaction, and system harnessing. Based on this framework, we systematically review recent progress in embodied morphology, embodied perception, world models, embodied planning, vision-language navigation, embodied manipulation, and embodied collaboration. We further identify long-horizon autonomy, predictive physical reasoning, test-time skill acquisition, and autonomous capability evolution as key challenges toward more general aerial embodied intelligence. Finally, we argue that harnessing physical-digital AI agents, through persistent coupling of digital intelligence with physical sensing, dynamics, action, and feedback, provides a system-level pathway toward adaptive and continuously evolving UAV autonomy. Project resources are available at our project website and GitHub repository.
Comments24 pages, 6 figures, 3 tables
