MAE Colloquium: Frederike Dümbgen (Carnegie Mellon)

Global Optimization for Robotics in the Age of Learning Optimization is the bedrock of robotics, and robotics is a great stress test for optimization methods. High dimensionality, nonconvexity, and nonsmoothness make many robotics problems notoriously hard to solve, whether with learning-based or model-based strategies. In this talk, I will discuss our recent advances in bringing learning-based techniques together with optimization principles. First, I will present extensions of moment-based optimization to data-driven, nonparametric problems that retain partial global optimality guarantees. I