论文/arXiv 2609.28816·1 个月前

FlyCNS：面向通信受限具身控制的基于连接组的信息组织

FlyCNS借鉴果蝇脑—神经索连接组组织局部计算与选择性通信，并在Unitree Go1仿真中验证受限通信下的全身协调控制。

AI 整理

• 硬件零部件
• 本体 SDK 与控制
• 仿真与训练

原文链接：FlyCNS: Connectome-Grounded Information Organization for Communication-Constrained Embodied Control（arXiv 2609.28816 · 发布日期：2026-09-23）

FlyCNS: Connectome-Grounded Information Organization for Communication-Constrained Embodied Control

FlyCNS借鉴果蝇脑—神经索连接组组织局部计算与选择性通信，并在Unitree Go1仿真中验证受限通信下的全身协调控制。

核心要点

• 分布式机器人身体常被集中处理，通信受限下需决定本地计算与传输信息。
• FlyCNS保留肢体局部感知运动计算，并通过上下行通路选择性远程通信。
• 其从真实连接组提取两类通路的方向结构复杂度，作为通信分配弱先验。
• 在Unitree Go1仿真中，通信收紧时性能退化更平滑；最严设置仍保持约0.882跟踪分。

验证范围与局限：验证范围为Unitree Go1仿真中的两种命令协议。

英文摘要 (Abstract)

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.

作者：Jinchang Zhang, Jiakai Lin, Guoyu Lu

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.28816：FlyCNS: Connectome-Grounded Information Organization for Communication-Constrained Embodied Control访问 2026-10-21