Paul Manns

Heidelberg University, Technische Universität Darmstadt, Technische Universität Braunschweig

Papers

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About

Paul Manns is a researcher whose work sits at the intersection of robotics, biomechanics, and optimal control, with a particular focus on developing computational tools that bridge simulation and real-world performance. His primary research areas include human-robot interaction, exoskeleton design, and efficient numerical methods for rigid-body dynamics. Manns’ most significant contribution is his work on motion optimization for lower-back exoskeletons, where he developed predictive models to reduce injury risk during lifting—a study that has garnered 43 citations and stands as his most influential publication. He has also advanced the field of autonomous systems for urban search and rescue, emphasizing reliability in high-stakes environments. Beyond application-specific work, Manns has made notable theoretical contributions to efficient derivative evaluation for rigid-body dynamics under kinematic constraints, enabling faster gradient-based optimization for complex robotic and biomechanical systems. His exploration of discrete mechanics and optimal control further demonstrates his commitment to rigorous, simulation-driven design. With a career spanning both foundational algorithms and applied robotics, Manns continues to shape how researchers model and optimize the interaction between humans and machines.