通过MeanFlow学习采样数据控制群体
Learning Sampled-data Control for Swarms via MeanFlow
AI总结：
本文提出基于MeanFlow的采样数据学习框架，用于大规模群体控制，通过有限时间系数和局部桥接监督信号实现高效学习，保证控制器符合线性时不变动力学。
摘要：
在通信或计算受限情况下，操控大规模群体会需要有限的控制更新，但大多数基于学习的方法未考虑此问题，而是建模瞬时速度场。为此，本文考虑最近的机器学习框架MeanFlow，并将其推广到一般线性动态系统设置。这导致了一个新的采样数据学习框架，直接在控制空间中运行，可用于群体操控。为此，我们学习了参数化每个区间最小能量控制的有限时间系数，并推导出将此量与局部桥接诱导监督信号联系起来的微分恒等式。此恒等式导致一个简单的stop-gradient回归目标，使区间系数场能够从桥接样本中高效学习。所学策略通过采样数据更新部署，保证所得到的控制器恰好符合规定的线性时不变动力学和执行通道。所提出的方法在大规模群体操控中实现少步操控，同时保持底层控制系统有限窗口执行结构的一致性。
英文摘要：
Steering large-scale swarms with only limited control updates is often needed due to communication or computational constraints, yet most learning-based approaches do not account for this and instead model instantaneous velocity fields. As a result, the natural object for decision making is a finite-window control quantity rather than an infinitesimal one. To address this gap, we consider the recent machine learning framework MeanFlow and generalize it to the setting with general linear dynamic systems. This results in a new sampled-data learning framework that operates directly in control space and that can be applied for swarm steering. To this end, we learn the finite-horizon coefficient that parameterizes the minimum-energy control applied over each interval, and derive a differential identity that connects this quantity to a local bridge-induced supervision signal. This identity leads to a simple stop-gradient regression objective, allowing the interval coefficient field to be learned efficiently from bridge samples. The learned policy is deployed through sampled-data updates, guaranteeing that the resulting controller exactly respects the prescribed linear time-invariant dynamics and actuation channel. The resulting method enables few-step swarm steering at scale, while remaining consistent with the finite-window actuation structure of the underlying control system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
