AERIS: 通过编排语言模型群在运行时实现空中边缘角色驱动智能
AERIS: Aerial-Edge Role-Driven Intelligence at Runtime via Orchestrated Language-Model Swarm
AI总结：
提出AERIS框架，通过编排专用小语言模型和轻量感知控制模块，在边缘实现空中平台的长时指令分解与实时闭环运行。
摘要：
将大型语言模型集成到机器人系统中有望增强自主性，但实际部署仍受严格的心跳约束调度和有限计算能力的限制。我们提出AERIS：一种用于空中平台的边缘部署框架。它将专用的小语言模型与轻量级感知和控制模块组织成可在运行时实例化的角色，并根据资源变化动态地在不同执行器之间重新绑定这些角色，从而将智能能力推向边缘。AERIS通过注意力-子目标对齐机制实现长时指令分解，该机制涉及在消息中注释当前活动的指令步骤，从而逐步接近长期目标。我们在高保真无人机视觉与语言导航基准上评估AERIS。在心跳定时执行机制下，AERIS在低频规划器和高频控制器之间维持稳定的感知-决策-控制循环，支持实时闭环操作。我们通过两个专注于规划和快速响应的实际实验进一步验证了其可部署性。补充材料中提供了演示视频。
英文摘要：
Integrating large language models into robotic systems holds promise for enhancing autonomy, yet practical deployment remains constrained by strict heartbeat-constrained scheduling and limited computational power. We propose AERIS: an edge deployment framework for aerial platforms. It organizes dedicated small language models combined with lightweight perception and control modules into roles that can be instantiated at runtime, and dynamically rebinds them across different executors as resources change, thereby pushing intelligent capabilities to the edge. AERIS achieves long-horizon instruction decomposition through an attention-subgoal alignment mechanism, which involves annotating the currently active instruction step in messages, thereby progressively approaching long-term objectives. We evaluate AERIS on a high-fidelity UAV Vision-and-Language Navigation benchmark. Under a heartbeat-timed execution mechanism, AERIS maintains a stable perception-decision-control loop between a low-frequency planner and a high-frequency controller, supporting real-time closed-loop operation. We further validate its deployability through two real-world experiments focused on planning and fast response. A demonstration video is provided in the supplementary materials.
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