Ulrike Kleb

FH JOANNEUM University of Applied Sciences

Papers

Total Citations

H-Index

About

Ulrike Kleb is a researcher at the forefront of human-robot collaboration, with a primary focus on ensuring safety in industrial environments through advanced motion prediction. Her most-cited work, "A Tensor‐based Regression Approach for Human Motion Prediction" (2022), introduces a novel computational framework that enables robotic systems to anticipate human movements in real time. By leveraging tensor-based regression, Kleb’s approach allows robots to detect potentially hazardous situations before they occur, moving beyond reactive safety measures toward proactive prevention. This work has garnered 6 citations, establishing a foundation for safer, more intuitive human-robot interaction in manufacturing and automation. Kleb’s contributions are particularly significant as collaborative robotics becomes central to Industry 4.0, where the ability to predict human motion is critical for both operational efficiency and worker safety. Her research bridges machine learning, robotics, and human factors, offering a data-driven pathway to reduce workplace accidents. For students and researchers, Kleb’s work exemplifies how mathematical modeling can directly address real-world safety challenges, making her a key figure in the evolution of trustworthy, human-aware robotic systems.