用高效机器学习实现光学系统自动对准，减少人工干预。
Automating Experimental Optics with Sample Efficient Machine Learning Methods
- 采用样本高效强化学习，降低数据需求。
- 在参数漂移条件下仍能实现稳定自动对准。
- 适合远程部署的复杂光学系统使用。
随着自由空间光学系统规模和复杂度的增长，故障排查变得愈发耗时，尤其在远程安装场景下可能难以实施。例如，高精细度光学谐振腔的对准极为敏感于输入光束模式，常需大量人力。本文展示如何利用机器学习实现自由空间光学谐振腔的自主模式匹配，仅需少量监督。所提出的算法通过样本高效的强化学习框架，在保持简单架构以利部署的同时，显著降低了数据需求。实验表明，该方法在存在实验参数漂移的情况下仍可实现自动化，适用于真实世界应用中不稳定环境下的光学系统控制。
原文摘要 · Abstract (English)
As free-space optical systems grow in scale and complexity, troubleshooting becomes increasingly time-consuming and, in the case of remote installations, perhaps impractical. An example of a task that is often laborious is the alignment of a high-finesse optical resonator, which is highly sensitive to the mode of the input beam. In this work, we demonstrate how machine learning can be used to achieve autonomous mode-matching of a free-space optical resonator with minimal supervision. Our approach leverages sample-efficient algorithms to reduce data requirements while maintaining a simple architecture for easy deployment. The reinforcement learning scheme that we have developed shows that automation is feasible even in systems prone to drift in experimental parameters, as may well be the case in real-world applications.
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