The Professorship of Intelligent Space and Energy Systems investigates a central question: How can Physical AI perceive, reason, and act reliably in safety-critical and data-scarce environments? We study how AI models can acquire new skills, adapt to changing conditions, perform long-horizon tasks, and remain robust when deployed beyond controlled laboratory settings. This includes methods for reinforcement learning, planning, uncertainty quantification, and validation before real-world operation.

The primary application domain covers areas related to the DLR Institute of Space Propulsion. In particular, this includes optimized space propulsion control and test operations, including autonomous robotic inspection after engine tests. Beyond these activities, we also conduct research on the application of these methods to autonomous spacecraft. Optimized energy management of the DLR site is envisaged as a future research topic.

Students interested in Physical AI, reinforcement learning, autonomous systems, and their applications are very welcome to join our courses or write their Bachelor’s or Master’s thesis with our group.