arXiv:2606.10771v1 Announce Type: cross Abstract: Reinforcement learning (RL)-based algorithms have recently emerged as a promising approach for adaptive optics (AO) control. In simulations and laboratory experiments, they have demonstrated robustness to real-world effects such as photon and detector noise, misregistration, vibrations, and rapid variations in seeing conditions. However, their performance has not yet been validated on sky. We report the first on-sky demonstration of a reinforcement learning controller for adaptive optics, named Policy Optimization for AO (PO4AO). We further ana
The increasing maturity of reinforcement learning algorithms and computational resources has made on-sky validation of theory and simulation possible for complex adaptive optics systems.
Demonstrating real-world application of advanced AI in scientific instrumentation accelerates discovery and provides a blueprint for autonomous systems in other demanding real-time control domains.
Adaptive optics control can now leverage the robustness and optimization capabilities of reinforcement learning, potentially overcoming limitations of traditional methods in varying atmospheric conditions.
- · Astronomical observatories
- · Reinforcement learning researchers
- · Space-based imaging
- · AI/ML hardware providers
- · Traditional adaptive optics control methods
- · Human operators in real-time calibration
Improved performance and efficiency of astronomical telescopes, enabling clearer and more detailed observations.
Accelerated development and application of autonomous AI controllers in other complex scientific or industrial systems, such as fusion reactors or drone swarms.
Enhanced scientific understanding of the universe due to better observational data, potentially leading to breakthroughs in astrophysics or exoplanet research.
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