天空中的记忆：基于多智能体记忆聚合的低空问答
Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation
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中文总结 AI 辅助
本文提出基于生成对抗性考试度量记忆质量，并开发以记忆为中心的MemCen框架，联合优化无人机选择与功率分配，实现低空问答高准确率。
中文摘要 AI 辅助
本文研究了低空问答（LAQA）问题，其中分布式无人机（UAV）的记忆在地面服务器上聚合，以回答关于长时间跨度的观测问题。与基于感知、通信、控制或计算指标的传统资源分配不同，LAQA需要一种明确的记忆价值度量。我们提出了一种生成对抗性考试（GAE），它利用前向模拟来评估记忆检索，并以考试成绩来量化记忆质量。这使得候选记忆的下游问答价值能够在无需访问黑盒字幕生成、检索和推理流水线内部机制的情况下被测量和优化。基于这一度量，我们开发了一个以记忆为中心（MemCen）的框架，该框架联合选择无人机并分配发射功率，以在通信约束下最大化记忆质量。在噪声受限的环境中，我们推导了一种基于QoM感知的封顶注水法则，该法则明确地将任务效用与物理层功率分配联系起来。我们进一步开发了惩罚连续优化（PSO）和学习记忆（L2M）求解器。在CARLA Town04和Town05中，MemCen在静态和动态通信条件下分别实现了92.4%和84.0%的问答准确率。在真实世界实验中，MemCen在全景多智能体系统（PMAS）基准上实现了88.5%的问答准确率。最后，无人机到机器狗演示进一步验证了所获取记忆在环境理解和导航中的实际效用。
英文摘要
This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories are aggregated at a ground server to answer questions about observations over a long horizon. Unlike conventional resource allocation based on sensing, communication, control, or computation metrics, LAQA requires an explicit measure of memory value. We propose a generative adversarial exam (GAE) that uses forward simulation to evaluate memory retrieval and exam scores to quantify memory quality. This enables the downstream QA value of candidate memories to be measured and optimized without accessing the internal mechanisms of the black-box captioning, retrieval, and reasoning pipeline. Building on this metric, we develop a memory-centric (MemCen) framework that jointly selects UAVs and allocates transmit power to maximize memory quality under communication constraints. In the noise-limited regime, we derive a QoM-aware capped water-filling law that explicitly connects task utility with physical-layer power allocation. We further develop penalty successive optimization (PSO) and learning to memorize (L2M) solvers. MemCen achieves QA accuracies of 92.4% and 84.0% in CARLA Town04 and Town05 under static and dynamic communication conditions, respectively. In real-world experiments, MemCen achieves 88.5% QA accuracy on the panoramic multi-agent system (PMAS) benchmark. Finally, UAV-to-robot-dog demonstrations further validate the practical utility of the acquired memories for environmental understanding and navigation.
发表机构
- The University of Hong Kong（香港大学）
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
- Southern University of Science and Technology（南方科技大学）
- University of Macau（澳门大学）
- Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）
机构由 AI 辅助整理，请以论文原文为准。
