LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller

2025/12/22 by Kirill Djebko, Djebko, Kirill, Tom Baumann +7
Engineering · #68T05 #93C40 (Primary) 93C95 (Secondary) #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.6 #I.2.8 #I.2.9 #Inertial Sensor and Navigation #Machine Learning (cs.LG) #Robotics (cs.RO) #Space Satellite Systems and Control #Spacecraft Dynamics and Control #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2512.19576

openalex publication_date 2025/12/22 · openalex created_date 2025/12/24 · openalex updated_date 2026/07/28

Abstract

Attitude control is essential for many satellite missions. Classical controllers, however, are time-consuming to design and sensitive to model uncertainties and variations in operational boundary conditions. Deep Reinforcement Learning (DRL) offers a promising alternative by learning adaptive control strategies through autonomous interaction with a simulation environment. Overcoming the Sim2Real gap, which involves deploying an agent trained in simulation onto the real physical satellite, remains a significant challenge. In this work, we present the first successful in-orbit demonstration of an AI-based attitude controller for inertial pointing maneuvers. The controller was trained entirely in simulation and deployed to the InnoCube 3U nanosatellite, which was developed by the Julius-Maximilians-Universität Würzburg in cooperation with the Technische Universität Berlin, and launched in January 2025. We present the AI agent design, the methodology of the training procedure, the discrepancies between the simulation and the observed behavior of the real satellite, and a comparison of the AI-based attitude controller with the classical PD controller of InnoCube. Steady-state metrics confirm the robust performance of the AI-based controller during repeated in-orbit maneuvers.

Citations

• Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic
• Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement\n Learning
• Limitations of Deep Neural Networks: a discussion of G. Marcus' critical appraisal of deep learning
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Proximal Policy Optimization Algorithms
• Continuous control with deep reinforcement learning

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