Papers
77
Total Citations
2,826
H-Index
22
About
Angela P. Schoellig is a leading robotics and control systems researcher whose work sits at the intersection of machine learning, safe autonomy, and real-world robot deployment. Best known for her highly cited 2022 review "Safe Learning in Robotics" (654 citations), she has been instrumental in shaping the field's understanding of how learning-based methods can be applied to robots while maintaining rigorous safety guarantees — a challenge central to bringing autonomous systems into real-world environments. Schoellig's contributions span aerial and ground robotics, with foundational work on the Flying Machine Arena platform and pioneering Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithms that enable mobile robots to improve path tracking through experience, even in demanding off-road conditions. Her research on distributed multi-robot trajectory generation and Bayesian optimization with safety constraints further demonstrates her commitment to scalable, reliable autonomy. A recurring theme across her work is the principled integration of learning and control: teaching robots to adapt quickly, generalize safely, and operate under uncertainty. With over 1,700 citations across her most influential papers alone, Schoellig's research has profoundly influenced both academic robotics and the broader conversation around trustworthy autonomous systems.
Research Focus
Key Achievements
Top Papers
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