Bio – Noémie Jaquier

Biography

Noémie Jaquier is an assistant professor at the KTH Royal Institute of Technology, where she heads the Geometric Robot Learning (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.

Noémie is the recipient of a WASP-AI/MLX professorship and a starting grant from the Swedish research council. She received several awards, notably the Best Presentation Award at CoRL’19, Best Paper Award Finalists (IROS’23, ICRA’24), the Hector-Stiftung Preis 2024 from the Heidelberg Academy of Sciences, and AI newcomer of Technical and Engineering Sciences 2023 by the German Federal Ministry of Education and Research.

Photo credits: Heidelberger Akademie der Wissenschaften

Education and Employment

2024 –	Assistant professor at the Division of Robotics, Perception and Learning, KTH Royal Institute of Technology
2020 – 2024	Postdoctoral Researcher at the High Performance Humanoid Technology (H²T) group, Karlsruhe Institute of Technology (KIT)
09/2023 – 02/2024	Visiting Postdoctoral Scholar at the Stanford Robotics Lab, Stanford University
2016 – 2020	D.Sc , Ecole Polytechnique Fédérale de Lausanne (EPFL) and Idiap Research Institute PhD thesis: “Robot Skills Learning with Riemannian Manifolds: Leveraging Geometry-awareness in Robot Learning, Optimization and Control”
04/2019 – 09/2019	PhD Sabbatical, Bosch Center for Artificial Intelligence (BCAI)
2014 – 2016	M.Sc. in Robotics and autonomous systems with minor in Computational Neurosciences, Ecole Polytechnique Fédérale de Lausanne (EPFL)
2011 – 2014	B.Sc. in Microengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)

Some highlights…

2024	Hector-Stiftung Preis, awarded by the Heidelberger Akademie der Wissenschaften and sponsored by the Hector Foundation
2024	Finalist for the IEEE ICRA Best Paper Award on Human-Robot Interaction for our paper “Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds”
2023	AI newcomer 2023 of Technical and Engineering Science, awarded by The Federal Ministry of Education and Research (BMBF) and German Informatics Society (GI)
2023	Finalist for the IROS Best Paper Award on Mobile Manipulation sponsored by OMRON Sinic X Corp for our paper “On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds”
2020	PhD thesis nominated for the EPFL Asea Brown Boveri Ltd. Award
2019	Idiap PhD Student award
2019	Best Presentation award at CoRL’19 for our paper “Bayesian Optimization Meets Riemannian Manifolds in Robot Learning”
2018	Robotics: Science and Systems (R:SS) 2018 Pioneer

Co-organized events

2024	ICRA’24 — Riemann and Gauss meet Asimov: 2nd tutorial on Geometric Methods in Robot Learning, Optimization, and Control
2023	IROS’23 — Workshop on Assistive Robotics for Citizens
2022	IROS’22 — Riemann and Gauss meet Asimov: A tutorial on Geometric Methods in Robot Learning, Optimization, and Control
2021	R:SS’21 — Workshop on Geometry and Topology in Robotics: Learning, Optimization, Planning, and Control
2020	IROS’20 — Workshop on Bringing Geometric Methods to Robot Learning, Optimization and Control

Teaching

WS 22/23	Riemannian methods for learning in robotics, KIT
WS 21/22	Riemannian methods for learning in robotics, KIT