发表机构
European Southern Observatory; Aalto University（欧洲南方天文台; 阿尔托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自适应光学控制中高非线性WFS导致的闭环数据采集难题，提出预校准的PO4AO策略，无需积分控制器即可直接在轨闭环操作，实现鲁棒控制学习。
AI 中文摘要
地基望远镜的高对比度成像需要极高精度的波前控制，控制器的性能最终取决于波前传感技术的质量与效率，这推动了灵敏度更高的波前传感器（WFS）的发展。但灵敏度提升的代价是非线性增强，给传统线性重构器与控制器带来挑战。强化学习（RL）是通过与环境交互学习控制策略的机器学习分支，近期在极端自适应光学（XAO）领域受到关注，此前研究表明基于模型的RL变体——自适应光学策略优化（PO4AO）可有效补偿时间延迟、配准误差及中等程度的WFS非线性。PO4AO通过收集线性重构器的积分控制器产生的闭环数据学习初始控制策略，但在WFS高度非线性及复杂工况下，积分控制器难以闭环，无法采集高保真数据，导致PO4AO学习鲁棒控制策略存在挑战。本文提出一种鲁棒学习策略：利用内部光源与变形镜（DM）对PO4AO进行预校准，使其无需稳定积分控制器引导初始模型，即可直接开展在轨闭环操作。
英文摘要
High contrast imaging with ground-based telescopes requires extremely precise wavefront control. The performance of the controller ultimately depends on the quality and efficiency of the wavefront sensing technique, which has led to the development of ever more sensitive wavefront sensors (WFS). However, the increase in sensitivity comes at the cost of greater nonlinearity, which poses challenges for conventional linear reconstructors and controllers. Reinforcement Learning (RL) is a branch of machine learning in which control policies are learned by interacting with the environment. RL has recently attracted interest in the field of XAO, and previous studies have demonstrated that a model-based RL variant, the Policy Optimization for Adaptive Optics (PO4AO), can effectively compensate for temporal delays, misregistration errors, and moderate WFS nonlinearities. PO4AO learns an initial control strategy by collecting closed-loop data from the integrator controller with a linear reconstructor. However, in cases of highly non-linear WFS and challenging conditions, closing the loop with the integrator is difficult, and high-fidelity data cannot be collected. Consequently, learning robust control with PO4AO is challenging. We propose a robust learning strategy in which PO4AO is pre-calibrated using an internal light source and a DM, enabling direct on-sky closed-loop operation without a stable integrator controller for bootstrapping the initial models.
Comments10 pages, 4 figures, SPIE Astronomical Telescope + Instrumentation 2026, Adaptive Optics Systems X
