为多智能体机器人问答设计内存质量优化的功率分配方案
Memory Centric Power Allocation for Multi-Agent Embodied Question Answering
- 用生成对抗评估模拟内存检索质量，量化内存价值
- 在通信受限下提升内存质量，性能优于现有方法
- 适合研究机器人协作与资源调度的学者参考
本文研究多智能体具身问答（MA-EQA），使机器人团队基于长时间观测回答问题。不同于传统侧重感知、通信或计算的边缘资源管理，MA-EQA聚焦聚合内存质量。为此，提出基于生成对抗评估（GAE）的质量内存（QoM）模型，利用前向模拟评估内存检索效果，并以评分量化QoM。基于该模型，设计内存中心功率分配（MCPA）方案，在通信资源约束下最大化内存质量。理论分析揭示了在噪声受限场景下，MCPA具有增强型截断水填结构。大量实验表明，MCPA在多种指标和场景下显著优于现有基准。
原文摘要 · Abstract (English)
This paper considers multi-agent embodied question answering (MA-EQA), which enables robot teams to answer queries based on their long-horizon observations. In contrast to existing edge resource management methods that optimize sensing, communication, or computation performance metrics, MA-EQA focuses on the quality of aggregated memory. To address this paradigm shift, we propose a quality of memory (QoM) model based on generative adversarial exam (GAE), which leverages forward simulation to evaluate memory retrieval and utilizes the resulting exam scores to quantify QoM. Based on the QoM model, we develop a memory-centric power allocation (MCPA) scheme that maximizes memory quality under communication resource constraints. Through analytical characterization in the noise-limited regime, we reveal a GAE-augmented capped water-filling structure for MCPA. Extensive experiments demonstrate that MCPA achieves significant improvements over existing benchmarks across diverse metrics and scenarios.
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