AeroWeaver：一种将空中技能编织进分布式自适应蜂群执行的具体化智能体框架
AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution
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中文总结 AI 辅助
AeroWeaver是一种具体化智能体框架，通过将无人机技能编织进协调的任务级行为，解决LLM智能体应用于无人机蜂群时的决策落地、全局-分布式协调和持续适应问题，实验验证了其有效性。
中文摘要 AI 辅助
集体智能是一种协作自主范式，其中多个智能体通过局部感知、信息交换和协调行动来追求共同目标。无人机蜂群通过协调多架无人机执行搜索、检查和跟踪等任务来体现这一范式。近期大语言模型（LLM）智能体的进展增强了自然语言任务理解和高层规划能力，为任务描述与集体行为之间提供了灵活的语义接口。尽管这些进展扩展了语义推理能力，但将LLM智能体应用于无人机蜂群仍面临挑战，包括将模型决策落地到可执行能力、协调全局任务推理与分布式执行，以及利用任务特定经验进行持续适应。为应对这些挑战，我们引入了AeroWeaver，一种具体化智能体框架，将单个无人机技能编织成协调的任务级行为。AeroWeaver将语义决策连接到受治理的技能，组织角色条件化的局部智能体以实现分布式协调，并利用角色索引的状态-动作-奖励经验在线优化技能选择。实验和运行时验证表明，AeroWeaver在测试条件下能保持有效的技能执行，支持无需中央智能体从全局上下文生成联合动作的机体本地多无人机操作，同时奖励引导的在线更新为从累积执行经验中自适应学习蜂群智能体提供了一条免训练路径。代码：此https URL。
英文摘要
Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and coordinated action. UAV swarms embody this paradigm by coordinating multiple vehicles in tasks such as search, inspection, and tracking. Recent advances in large language model (LLM) agents have strengthened natural-language task understanding and high-level planning, providing a flexible semantic interface between mission descriptions and collective behavior. While these advances expand semantic reasoning, applying LLM agents to UAV swarms raises challenges in grounding model decisions in executable capabilities, reconciling global task reasoning with distributed execution, and using mission-specific experience for continual adaptation. To address these challenges, we introduce AeroWeaver, an embodied-agent harness that weaves individual UAV skills into coordinated mission-level behavior. AeroWeaver connects semantic decisions to governed skills, organizes role-conditioned local agents for distributed coordination, and uses role-indexed state-action-reward experience to refine skill selection online. Experiments and runtime validation show that AeroWeaver maintains valid skill execution under tested conditions and supports body-local multi-UAV operation without a central agent generating joint actions from global context, while reward-guided online updates provide a training-free path for adaptive learning swarm agents from accumulated execution experience. Code: https://github.com/Admire-ljb/AeroWeaver.
发表机构
- Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
