发表机构
Indiana University Bloomington（印第安纳大学布卢明顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FlyCNS受果蝇连接组启发，通过分离上下行通路组织信息，在通信受限下用约21-22%通信量保持高跟踪性能，实现局部与全局协调平衡。
AI 中文摘要
机器人身体在感知和驱动方面本质上是分布式的，然而基于学习的控制通常仍依赖于集中式信息处理。本研究探讨了通信受限的具身控制中的信息组织问题：哪些计算应保持局部化，哪些信息值得传输以实现全身协调。我们提出了FlyCNS，一个受果蝇脑-神经索连接组启发的具身信息组织框架。FlyCNS在每个肢体内部保留局部感觉运动计算，并通过分离的上行和下行路由通路实现选择性长距离通信。从真实连接组中，FlyCNS提取了这两种通路类型的定向结构复杂性，并将其作为通信分配的弱先验，而消息内容、传输时机和运动策略则保持任务自适应，并通过强化学习进行学习。在Unitree Go1仿真中，随着通信预算收紧，FlyCNS展现出更优雅的性能退化。在最受限的设置下，它仅使用全通信参考的约21-22%的通信量，同时在两种命令协议下仍保持约0.882的跟踪分数，与全通信参考的差距不超过6.1%。这些结果表明，真实神经连接组不仅能为控制网络的结构设计提供信息，还能为跨具身的信息组织提供可迁移的归纳偏置，指导机器人在有限通信资源下平衡局部计算与长距离协调。
英文摘要
Robotic bodies are inherently distributed in sensing and actuation, yet learning-based control still commonly relies on centralized information processing. This work studies the problem of information organization in communication-constrained embodied control: which computations should remain local, and which information is worth transmitting for whole-body coordination. We propose FlyCNS, an embodied information-organization framework inspired by the Drosophila brain--nerve-cord connectome. FlyCNS preserves local sensorimotor computation within each limb and enables selective long-range communication through separate ascending and descending routing pathways. From a real connectome, FlyCNS extracts the directional structural complexity of these two pathway types and uses it as a weak prior over communication allocation, while message content, transmission timing, and locomotion policies remain task-adaptive and are learned through reinforcement learning. In Unitree Go1 simulation, FlyCNS exhibits more graceful performance degradation as the communication budget is tightened. Under the most restrictive setting, it uses only about 21--22\% of the communication of the full-communication reference, while still maintaining a tracking score of approximately 0.882 under both command protocols, with a gap of no more than 6.1\% from the full-communication reference. These results indicate that real neural connectomes can inform not only the structural design of control networks, but also provide transferable inductive biases for information organization across embodiments, guiding robots in balancing local computation and long-range coordination under limited communication resources.
