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Reinforcement learning controller successfully demonstrated on telescope

By PulseAugur Editorial · [2 sources] ·

Researchers have successfully demonstrated a reinforcement learning (RL) controller for adaptive optics (AO) systems on a telescope for the first time. The controller, named PO4AO, was deployed on the Papyrus system at the OHP and consistently outperformed traditional controllers. It showed robustness to noise and vibrations, operating effectively across various observing conditions and targets. AI

IMPACT Demonstrates the practical application of RL in complex real-world systems, potentially improving astronomical observations.

RANK_REASON The cluster reports on a new research paper detailing the first on-sky demonstration of a reinforcement learning controller for adaptive optics.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jalo Nousiainen, Vincent Chambouleyron, Benoit Neichel, Sylvain Cetre, Jean-Francois Sauvage, Angelie Alagao, Markus Kasper, Jonathan Dray, Romain Fetick, Byron Engler ·

    On-sky demonstration of reinforcement learning for adaptive optics control

    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 a…

  2. arXiv cs.LG TIER_1 English(EN) · Byron Engler ·

    On-sky demonstration of reinforcement learning for adaptive optics control

    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, vibr…

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