Preprints
S. Kim and N. Jaquier. “ChainSplat: A Physics-Inspired Screw-Theoretic Model for Learning Deformable Linear Object Dynamics from Multi-View RGB Videos “, arXiv preprint arXiv:2608.28570, 2026.
R. Pérez-Dattari, F. Leiva, A. Testa, L. Rozo, J. Ruiz del Solar, and N. Jaquier. “Let the Dynamics Flow: Stable Flow Matching Dynamical Systems “, arXiv preprint arXiv:2606.03834, 2026.
L. Hadjiloizou, R. Pérez-Dattari, and N. Jaquier. “Symmetries Here and There, Combined Everywhere: Cross-space Symmetry Compositions in Robotics “, arXiv preprint arXiv:2605.22639, 2026.
K. Friedl, N. Jaquier, S. Kim, J. Lundell, and D. Kragic. “Reduced-order Control and Geometric Structure of Learned Lagrangian Latent Dynamics“, arXiv preprint arXiv:2602.08963, 2026.
2027
N. Jaquier and L. Rozo. “Riemannian Manifolds in Robot Learning, Optimization, and Control“, Cambridge University Press, 2027.
2026
M. Welle*, N. Jaquier*, A. Gams*, J. Lundell, and D. Kragic. “Transfer learning in robotics: From promises to practice through the emerging role of foundation models“, Science Robotics 11 (113), eaeh4374, 2026.
F. Pavesi, A. Candelieri, and N. Jaquier. “Information Theoretic Bayesian Optimization over the Probability Simplex“, Conference on Uncertainty in Artificial Intelligence (UAI) 337, pp.5356-5375, 2026.
K. Friedl, N. Jaquier, M. Liao, and D. Kragic. “Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach“, Intl. Conf. on Machine Learning (ICML), 2026.
L. Augenstein*, N. Jaquier*, T. Asfour and L. Rozo. “Taxonomy-aware Dynamic Motion Generation on Hyperbolic Manifolds“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), 2026.
2025
N. Jaquier*, M. Welle*, A. Gams, K. Yao, B. Fichera, A. Billard, A. Ude, T. Asfour, and D. Kragic. “Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges“, International Journal of Robotics Research (IJRR) 44(3), pp. 465-485, 2025.
H. Ding, N. Jaquier, J. Peters, and L. Rozo. “Fast and Robust Visuomotor Riemannian Flow Matching Policy“, IEEE Transactions on Robotics, 41, pp. 5327-5343, 2025.
P. Mostowsky, V. Dutordoir, I. Azangulov, N. Jaquier, M. J. Hutchinson, A. Ravuri, L. Rozo, A. Terenin, V. Borovitskiy. “The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs“, Journal of Machine Learning Research (JMLR), 26(276):1−14, 2025.
K. Friedl, N. Jaquier, J. Lundell, T. Asfour and D. Kragic. “A Riemannian Framework for Learning Reduced-Order Lagrangian Dynamics“, in Intl. Conf. on Learning Representations (ICLR), 2025.
L. Rozo*, M. González-Duque*, N. Jaquier, and S. Hauberg. “Riemann2: Learning Riemannian Submanifolds from Riemannian Data“, in Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), 2025.
H. Ding, A. Duan, Z. Sun, L. Rozo, N. Jaquier, D. Song, and Y. Nakamura. “Towards Safe Imitation Learning via Potential Field-Guided Flow Matching“, in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 11693-11700, 2025.
2024
N. Jaquier, L. Rozo, M. González-Duque, V. Borovitskiy, and T. Asfour. “Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic Manifolds“, in Intl. Conf. on Machine Learning (ICML), 2024.
M. Braun, N. Jaquier, L. Rozo, and T. Asfour. “Riemannian Flow Matching Policy for Robot Motion Learning“, in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 5144-5151, 2024.
N. Jaquier*, L. Rozo*, and T. Asfour. “Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot Learning“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 242-249, 2024.
T. Daab, N. Jaquier, C. Dreher, A. Meixner, F. Krebs, and T. Asfour. “Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 2317-2323, 2024.
Finalist for the IEEE ICRA Best Paper Award on Human-Robot Interaction.
J. Gao, X. Jin, F. Krebs, N. Jaquier, and T. Asfour. “Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 16850-16857, 2024.
A. Meixner, M. Carl, F. Krebs, N. Jaquier, and T. Asfour. “Towards Unifying Human-Likeness: Evaluating Metrics for Human-Like Motion Retargeting on Bimanual Manipulation Tasks“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 13015-13022, 2024.
2023
J. Gao, Z. Tao, N. Jaquier, and T. Asfour. “K-VIL: Keypoints-based Visual Imitation Learning“, IEEE Transactions on Robotics, 39(5), pp. 3888-3908, 2023.
H. Klein, N. Jaquier, A. Meixner, and T. Asfour. “On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds“, in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS, pp.2346-2353, 2023.
Finalist for the IROS Best Paper Award on Mobile Manipulation sponsored by OMRON Sinic X Corp.
2022
N. Jaquier, Y. Zhou, J. Starke, and T. Asfour. “Learning to Sequence and Blend Robot Skills via Differentiable Optimization“, IEEE Robotics and Automation Letters, 7(3), pp.8431-8438, 2022.
H. Klein, N. Jaquier, A. Meixner, and T. Asfour. “A Riemannian Take on Human Motion Analysis and Retargeting“, in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp.5210-5217, 2022.
2021
N. Jaquier, L. Rozo, D. G. Caldwell and S. Calinon. “Geometry-aware Manipulability Learning, Tracking and Transfer“, International Journal of Robotics Research (IJRR), 20:2-3, pp.624-650, 2021.
N. Jaquier, R. Haschke and S. Calinon. “Tensor-variate Mixture of Experts for Proportional Myographic Control of a Robotic Hand“, Robotics and Autonomous Systems, 142, 2021.
2020
N. Jaquier. “Robot skills learning with Riemannian manifolds: Leveraging geometry-awareness in robot learning, optimization and control“, PhD thesis, Ecole Polytechnique Fédérale de Lausanne (EPFL), 2020.
Nominated for the Asea Brown Boveri Ltd. Award.
N. Jaquier, L. Rozo and S. Calinon. “Analysis and Transfer of Human Movement Manipulability in Industry-like Activities“, in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp.11131-11138, 2020.
H. Girgin, E. Pignat, N. Jaquier and S. Calinon. “Active Improvement of Control Policies with Bayesian Gaussian Mixture Model“, in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 5395-5401, 2020.
2019
N. Jaquier, L. Rozo, S. Calinon and M. Bürger. “Bayesian Optimization Meets Riemannian Manifolds in Robot Learning“, In Conference on Robot Learning (CoRL), 2019.
Oral presentation, CoRL’19 Best presentation award.
N. Jaquier, D. Ginsbourger and S. Calinon. “Learning from demonstration with model-based Gaussian process“, In Conference on Robot Learning (CoRL), 2019.
2018
N. Jaquier*, L. Rozo*, D. G. Caldwell and S. Calinon. “Geometry-aware Tracking of Manipulability Ellipsoids“, in Robotics: Science and Systems (R:SS), 2018.
2017
N. Jaquier and S. Calinon. “Gaussian mixture regression on symmetric positive definite matrices manifolds: Application to wrist motion estimation with sEMG“, in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp.59-64, 2017.
L. Rozo, N. Jaquier, S. Calinon and D. G. Caldwell. “Learning manipulability ellipsoids for task compatibility in robot manipulation“, in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp.3183-3189, 2017.
N. Jaquier, C. Castellini and S. Calinon. “Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds“, in Myoelectric control (MEC) Symposium, 2017.
