Frederike Dümbgen is an incoming assistant professor at Carnegie Mellon University, Mechanical Engineering. She is currently a researcher in the Willow team of Inria Paris, where she has been working on optimization for robotics since May 2024 under the Marie Curie Postdoctoral Fellowship LiftMeUp. Before joining Inria, she spent two years as a postdoctoral fellow at the Robotics Institute of the University of Toronto, collaborating with Prof. Timothy D. Barfoot on certifiable optimization. Frederike holds a Ph.D. in Computer and Communication Sciences from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where her research focused on developing systems for non-visual spatial perception using diverse sensor modalities. She earned her B.Sc. and M.Sc. in Mechanical Engineering from EPFL in 2013 and 2016, respectively, with a minor in Computational Science and Engineering. She performed her Master’s thesis at the Autonomous Systems Lab of ETH Zürich and gained experience in industry as an intern at Disney Research and ABB, among others. She was recognized as a R:SS Pioneer in 2024, as Google’s Women Techmaker in 2020, and has served as associate co-chair of the RAS technical committee on model-based optimization since 2024.
Talk Title: A View of Robotics Through the Global Optimization Lens
Abstract:
Optimization is omnipresent in robotics, be it in online estimation and control, or in offline policy learning. In the face of high search dimensions, non-smoothness, and real-time constraints, practitioners often resort to local solvers and abandon ambitions to reach globally optimal solutions. In this talk, I will give an overview of our recent works on searching for global solutions in such adversarial settings. I will cover our efforts to make semidefinite relaxations of polynomial problems more practical, recent extensions to non-polynomial and non-parametric settings, and finally, recent progress in improving standard solvers by combining foundation models and global optimization.
RPL Summer School 2026 