InformatiKOM I, Bldg. 50.19, Atrium, Adenauerring 12

29. July 2025, 13:00

Noémie Jaquier is an assistant professor at the KTH Royal Institute of Technology, where she heads the Geometric Robot (GeoRob) Lab at the Division of Robotics, Perception and Learning. She received her PhD degree from the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland in 2020. Prior to joining KTH, she was a postdoctoral researcher in the High Performance Humanoid Technologies Lab (H²T) at the Karlsruhe Institute of Technology (KIT) and a visiting postdoctoral scholar at the Stanford Robotics Lab. Her research investigates data-efficient and theoretically-sound learning algorithms that leverage differential geometry- and physics-based inductive bias to endow robots with close-to-human learning and adaptation capabilities.

Abstract:

Lagrangian mechanics provides a powerful framework for modeling the dynamics of physical systems by inferring their motions based on energy conservation. This talk will explore recent advances in applying geometric perspectives, particularly Riemannian geometry, to Lagrangian principles for predicting and optimizing motion dynamics. First, I will discuss how the dynamic properties of humans and robots are straightforwardly accounted for by considering geometric configuration spaces. Second, I will show how this geometric approach can be extended to generate dynamic-aware, collision-free robot motions by modifying the underlying Riemannian metric. Finally, I will consider the problem of learning unknown high-dimensional Lagrangian dynamics. I will present a geometric architecture to learn physically-consistent and interpretable reduced-order dynamic parameters that accurately capture the behavior of the original system.