Towards on-sky adaptive optics control using reinforcement learning

• URL: http://arxiv.org/abs/2205.07554v1
• Date: Mon, 16 May 2022 10:01:06 GMT
• Title: Towards on-sky adaptive optics control using reinforcement learning
• Authors: J. Nousiainen, C. Rajani, M. Kasper, T. Helin, S. Y. Haffert, C. V\'erinaud, J. R. Males, K. Van Gorkom, L. M. Close, J. D. Long, A. D. Hedglen, O. Guyon, L. Schatz, M. Kautz, J. Lumbres, A. Rodack, J.M. Knight, K. Miller
• Abstract summary: The direct imaging of potentially habitable Exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilohertz to several kilohertz. Most of the habitable exoplanets are located at small angular separations from their host stars, where the current XAO systems' control laws leave strong residuals.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The direct imaging of potentially habitable Exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilohertz to several kilohertz. Most of the habitable exoplanets are located at small angular separations from their host stars, where the current XAO systems' control laws leave strong residuals.Current AO control strategies like static matrix-based wavefront reconstruction and integrator control suffer from temporal delay error and are sensitive to mis-registration, i.e., to dynamic variations of the control system geometry. We aim to produce control methods that cope with these limitations, provide a significantly improved AO correction and, therefore, reduce the residual flux in the coronagraphic point spread function. We extend previous work in Reinforcement Learning for AO. The improved method, called PO4AO, learns a dynamics model and optimizes a control neural network, called a policy. We introduce the method and study it through numerical simulations of XAO with Pyramid wavefront sensing for the 8-m and 40-m telescope aperture cases. We further implemented PO4AO and carried out experiments in a laboratory environment using MagAO-X at the Steward laboratory. PO4AO provides the desired performance by improving the coronagraphic contrast in numerical simulations by factors 3-5 within the control region of DM and Pyramid WFS, in simulation and in the laboratory. The presented method is also quick to train, i.e., on timescales of typically 5-10 seconds, and the inference time is sufficiently small (< ms) to be used in real-time control for XAO with currently available hardware even for extremely large telescopes.

Related papers

• Neural-based Control for CubeSat Docking Maneuvers [0.0]
  This paper presents an innovative approach employing Artificial Neural Networks (ANN) trained through Reinforcement Learning (RL) The proposed strategy is easily implementable onboard and offers fast adaptability and robustness to disturbances by learning control policies from experience. Our findings highlight the efficacy of RL in assuring the adaptability and efficiency of spacecraft RVD, offering insights into future mission expectations.
  arXiv  Detail & Related papers  (2024-10-16T16:05:46Z)
• LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting [50.808933338389686]
  LiDAR simulation plays a crucial role in closed-loop simulation for autonomous driving. We present LiDAR-GS, the first LiDAR Gaussian Splatting method, for real-time high-fidelity re-simulation of LiDAR sensor scans in public urban road scenes. Our approach succeeds in simultaneously re-simulating depth, intensity, and ray-drop channels, achieving state-of-the-art results in both rendering frame rate and quality on publically available large scene datasets.
  arXiv  Detail & Related papers  (2024-10-07T15:07:56Z)
• Efficient Camera Exposure Control for Visual Odometry via Deep Reinforcement Learning [10.886819238167286]
  This study employs a deep reinforcement learning framework to train agents for exposure control. A lightweight image simulator is developed to facilitate the training process. Different levels of reward functions are crafted to enhance the VO systems.
  arXiv  Detail & Related papers  (2024-08-30T04:37:52Z)
• Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control [0.565395466029518]
  We implement and adapt an RL method called Policy Optimization for AO (PO4AO) to the GHOST test bench at ESO headquarters. We study the predictive and self-calibrating aspects of the method. New implementation on GHOST running PyTorch introduces only around 700 microseconds in addition to hardware, pipeline, and Python interface latency.
  arXiv  Detail & Related papers  (2023-12-30T14:11:43Z)
• Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays [5.977871949434069]
  Local-remote systems allow robots to execute complex tasks in hazardous environments. Time delays can compromise system performance and stability. We introduce an adaptive control method employing reinforcement learning to tackle the time-delayed control problem.
  arXiv  Detail & Related papers  (2023-05-26T14:34:45Z)
• Real-Time Model-Free Deep Reinforcement Learning for Force Control of a Series Elastic Actuator [56.11574814802912]
  State-of-the art robotic applications utilize series elastic actuators (SEAs) with closed-loop force control to achieve complex tasks such as walking, lifting, and manipulation. Model-free PID control methods are more prone to instability due to nonlinearities in the SEA. Deep reinforcement learning has proved to be an effective model-free method for continuous control tasks.
  arXiv  Detail & Related papers  (2023-04-11T00:51:47Z)
• Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors [64.68349896377629]
  This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to 2m distance.
  arXiv  Detail & Related papers  (2023-03-03T14:14:08Z)
• Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems [79.67879934935661]
  We study the problem of system identification and adaptive control of unknown ARX systems. We provide finite-time learning guarantees for the ARX systems under both open-loop and closed-loop data collection.
  arXiv  Detail & Related papers  (2021-08-26T18:00:00Z)
• Self-optimizing adaptive optics control with Reinforcement Learning for high-contrast imaging [0.0]
  We describe how model-free Reinforcement Learning can be used to optimize a Recurrent Neural Network controller for closed-loop predictive control. We show in simulations that our algorithm can also be applied to the control of a high-order deformable mirror.
  arXiv  Detail & Related papers  (2021-08-24T10:02:55Z)
• Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
  We propose a novel optimization backbone for visual SLAM systems. We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems. Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
  arXiv  Detail & Related papers  (2020-11-02T18:02:26Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.