重构机器人通信结构比增大模型能带来更大协同效率提升
Structured interactions improve distributed coordination beyond model scaling in a real-world multi-robot system
- 用分层模块化通信结构替代全连接，提升系统协同性能
- 结构优化使性能提升47分（满分100），远超模型扩容的9分
- 适合关注多机器人系统设计的工程师与研究者
在硬件预算相同的情况下，本文探讨了真实世界多机器人协同中的系统级设计问题：相比增加单机模型规模，重新设计机器人间通信结构能否带来更大收益？通过10台物理机器人完成运输与建图任务（每种条件5次运行，共60次实验），发现从全连接转向分层模块化交互，可使归一化性能提升47分（0-100），而将神经网络隐藏层大小翻倍最多仅提升9分。嵌套混合效应模型分析显示，拓扑结构对模型拟合度的提升远大于规模扩展。该结论在独立SMAC复现中得到验证；异构基准重分析提供次要支持，非主证据。仿真校准外推显示，隐藏单元超过1024后性能趋于饱和，但未在真实硬件上直接观测。结果表明，在所测试系统与任务下，交互结构可能起主导作用，但更广泛的量化泛化仍需进一步验证。
原文摘要 · Abstract (English)
Scaling individual robot capabilities is common but costly. Here we investigate a system-level design question in real-world multi-robot coordination: given matched hardware budgets, does restructuring communication among robots yield larger gains than increasing onboard model size? Using a representative transport-and-mapping task with 10 physical robots (5 runs per condition, 60 runs total), we find that switching from fully connected to modular hierarchical interactions improves normalised performance by 47 points (0--100), whereas doubling neural network hidden size yields at most 9 points. Nested mixed-effects model comparisons show a substantially larger improvement in model fit for topology than for scale. The pattern is confirmed in independent SMAC replications; heterogeneous benchmark reanalyses provide secondary supporting consistency checks rather than primary evidence. Performance saturation beyond 1024 hidden units is observed in simulation-calibrated extrapolation, not directly on hardware. These results indicate that interaction structure can play a dominant role within the tested system and task setting, while broader quantitative generalisation remains to be established.
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