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

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 offers a promising alternative by learning adaptive control strategies through autonomous interaction with a simulation environment. Overcoming the simulation-to-reality 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 artificial intelligence-based attitude controller for inertial pointing maneuvers overcoming the simulation-to-reality gap. The controller was trained entirely in simulation and deployed to the InnoCube three-unit 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 artificial intelligence agent design, the training methodology, and a characterization of the discrepancies between the simulation environment and the in-orbit behavior of the satellite. To provide a reference, we evaluate the in-orbit performance of the artificial intelligence-based controller against both simulation results and the in-orbit performance of InnoCube’s default proportional-derivative controller under identical mission conditions. Steady-state metrics confirm the robust performance of the artificial intelligence-based controller during repeated in-orbit maneuvers.