Michael Hillebrand

Fraunhofer Institute for Mechatronic Systems Design

Papers

Total Citations

H-Index

About

Michael Hillebrand's research focuses on advancing autonomous systems through deep reinforcement learning and rigorous systems engineering, with particular emphasis on space robotics and mobile robot navigation. His most cited work, "A design methodology for deep reinforcement learning in autonomous systems" (2020, 13 citations), addresses one of the fundamental challenges in autonomous mobile robotics—enabling robots to learn navigation in complex environments for applications ranging from industrial production to hostile settings like space. This methodology provides a structured approach for integrating deep reinforcement learning into autonomous systems, bridging the gap between theoretical algorithms and practical deployment. In his earlier work, "Specification Technique for Virtual Testbeds in Space Robotics" (2018, 6 citations), Hillebrand tackles the engineering complexity of space mission development, proposing systematic validation approaches to manage multidisciplinary dependencies and diverse operational conditions. His contributions are particularly valuable for researchers and engineers developing reliable autonomous systems for high-stakes environments, where verification and learning must coexist. Hillebrand's work demonstrates a commitment to making autonomous systems more capable and trustworthy through principled design methodologies.