{"id":74,"date":"2024-11-02T19:52:13","date_gmt":"2024-11-02T19:52:13","guid":{"rendered":"https:\/\/njaquier.ch\/?page_id=74"},"modified":"2026-08-31T18:10:30","modified_gmt":"2026-08-31T18:10:30","slug":"research","status":"publish","type":"page","link":"https:\/\/njaquier.ch\/?page_id=74","title":{"rendered":"Publications"},"content":{"rendered":"\n

Preprints<\/h2>\n\n\n\n
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S. Kim and N. Jaquier.\u00a0“ChainSplat: A Physics-Inspired Screw-Theoretic Model for Learning Deformable Linear Object Dynamics from Multi-View RGB Videos <\/strong>“, arXiv preprint arXiv:2608.28570, 2026.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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R. P\u00e9rez-Dattari, F. Leiva, A. Testa, L. Rozo, J. Ruiz del Solar, and N. Jaquier. “Let the Dynamics Flow: Stable Flow Matching Dynamical Systems <\/strong>“, arXiv preprint arXiv:2606.03834, 2026.<\/p>\n\n\n\n

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L. Hadjiloizou, R. P\u00e9rez-Dattari, and N. Jaquier. “Symmetries Here and There, Combined Everywhere: Cross-space Symmetry Compositions in Robotics <\/strong>“, arXiv preprint arXiv:2605.22639, 2026.<\/p>\n\n\n\n

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K. Friedl, N. Jaquier, S. Kim, J. Lundell, and D. Kragic. “Reduced-order Control and Geometric Structure of Learned Lagrangian Latent Dynamics<\/strong>“, arXiv preprint arXiv:2602.08963, 2026.<\/p>\n\n\n\n

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L. Augenstein, N. Jaquier, T. Asfour and L. Rozo. “On Probabilistic Pullback Metrics for Latent Hyperbolic Manifolds<\/strong>“, arXiv preprint arXiv:2410.20850, 2024.<\/p>\n\n\n\n

pdf<\/a> \u2014 code<\/a><\/p>\n<\/div><\/div>\n\n\n\n

2027<\/h2>\n\n\n\n
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N. Jaquier and L. Rozo. “Riemannian Manifolds in Robot Learning, Optimization, and Control<\/strong>“, Cambridge University Press, 2027.<\/p>\n\n\n\n

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2026<\/h2>\n\n\n\n
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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<\/strong>“, Science Robotics 11 (113), eaeh4374, 2026.<\/p>\n\n\n\n

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F. Pavesi, A. Candelieri, and N. Jaquier. “Information Theoretic Bayesian Optimization over the Probability Simplex<\/strong>“, Conference on Uncertainty in Artificial Intelligence (UAI) 337, pp.5356-5375, 2026.<\/p>\n\n\n\n

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K. Friedl, N. Jaquier, M. Liao, and D. Kragic. “Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach<\/strong>“, Intl. Conf. on Machine Learning (ICML), 2026.<\/p>\n\n\n\n

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L. Augenstein*, N. Jaquier*, T. Asfour and L. Rozo. “Taxonomy-aware Dynamic Motion Generation on Hyperbolic Manifolds<\/strong>“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), 2026.<\/p>\n\n\n\n

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2025<\/h2>\n\n\n\n
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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<\/strong>“, International Journal of Robotics Research (IJRR) 44(3), pp. 465-485, 2025.<\/p>\n\n\n\n

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H. Ding, N. Jaquier, J. Peters, and L. Rozo. “Fast and Robust Visuomotor Riemannian Flow Matching Policy<\/strong>“, IEEE Transactions on Robotics, 41, pp. 5327-5343, 2025.<\/p>\n\n\n\n

pdf<\/a> \u2014 website & video<\/a><\/p>\n<\/div>

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P. Mostowsky, V. Dutordoir, I. Azangulov, N. Jaquier, M. J. Hutchinson, A. Ravuri, L. Rozo, A. Terenin, V. Borovitskiy. “The GeometricKernels Package: Heat and Mat\u00e9rn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs<\/strong>“, Journal of Machine Learning Research (JMLR), 26(276):1\u221214, 2025.<\/p>\n\n\n\n

pdf<\/a> \u2014 code<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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K. Friedl, N. Jaquier, J. Lundell, T. Asfour and D. Kragic. “A Riemannian Framework for Learning Reduced-Order Lagrangian Dynamics<\/strong>“, in Intl. Conf. on Learning Representations (ICLR), 2025.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & video<\/a><\/p>\n<\/div>

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L. Rozo*, M. Gonz\u00e1lez-Duque*, N. Jaquier, and S. Hauberg. “Riemann2<\/sup>: Learning Riemannian Submanifolds from Riemannian Data<\/strong>“, in  Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), 2025.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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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<\/strong>“, in IEEE\/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 11693-11700, 2025.<\/p>\n\n\n\n

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2024<\/h2>\n\n\n\n
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N. Jaquier, L. Rozo, M. Gonz\u00e1lez-Duque, V. Borovitskiy, and T. Asfour. “Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic Manifolds<\/strong>“, in Intl. Conf. on Machine Learning (ICML), 2024.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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M. Braun, N. Jaquier, L. Rozo, and T. Asfour. “Riemannian Flow Matching Policy for Robot Motion Learning<\/strong>“, in IEEE\/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 5144-5151, 2024.<\/p>\n\n\n\n

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N. Jaquier*, L. Rozo*, and T. Asfour. “Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot Learning<\/strong>“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 242-249, 2024.<\/p>\n\n\n\n

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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<\/strong>“, 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.<\/mark><\/p>\n\n\n\n

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J. Gao, X. Jin, F. Krebs, N. Jaquier, and T. Asfour. “Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks<\/strong>“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 16850-16857, 2024.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & videos<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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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<\/strong>“, in IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 13015-13022, 2024.<\/p>\n\n\n\n

pdf<\/a> \u2014 video<\/a><\/p>\n<\/div>

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2023<\/h2>\n\n\n\n
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J. Gao, Z. Tao, N. Jaquier, and T. Asfour. “K-VIL: Keypoints-based Visual Imitation Learning<\/strong>“, IEEE Transactions on Robotics, 39(5), pp. 3888-3908, 2023.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & videos<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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H. Klein, N. Jaquier, A. Meixner, and T. Asfour. “On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds<\/strong>“, 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.<\/mark><\/p>\n\n\n\n

pdf<\/a> \u2014 video<\/a><\/p>\n<\/div>

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A. Meixner, F. Krebs, N. Jaquier, and T. Asfour. “An Evaluation of Action Segmentation Algorithms on Bimanual Manipulation Datasets<\/strong>“, in IEEE\/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 4912-4919, 2023.<\/p>\n\n\n\n

pdf<\/a> \u2014 video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

2022<\/h2>\n\n\n\n
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N. Jaquier, Y. Zhou, J. Starke, and T. Asfour. “Learning to Sequence and Blend Robot Skills via Differentiable Optimization<\/strong>“, IEEE Robotics and Automation Letters, 7(3), pp.8431-8438, 2022.<\/p>\n\n\n\n

pdf<\/a> \u2014 code<\/a> \u2014 video<\/a><\/p>\n<\/div>

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N. Jaquier and T. Asfour. “Riemannian geometry as a unifying theory for robot motion learning and control<\/strong>“, in International Symposium on Robotics Research (ISRR) – Blue sky track, 2022.<\/p>\n\n\n\n

pdf<\/a> \u2014 video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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H. Klein, N. Jaquier, A. Meixner, and T. Asfour. “A Riemannian Take on Human Motion Analysis and Retargeting<\/strong>“, in IEEE\/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp.5210-5217, 2022.<\/p>\n\n\n\n

pdf<\/a> \u2014 webpage & video<\/a><\/p>\n<\/div>

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2021<\/h2>\n\n\n\n
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N. Jaquier, L. Rozo, D. G. Caldwell and S. Calinon. “Geometry-aware Manipulability Learning, Tracking and Transfer<\/strong>“, International Journal of Robotics Research (IJRR), 20:2-3, pp.624-650, 2021.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & videos<\/a> <\/p>\n<\/div><\/div>\n\n\n\n

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N. Jaquier, R. Haschke and S. Calinon. “Tensor-variate Mixture of Experts for Proportional Myographic Control of a Robotic Hand<\/strong>“, Robotics and Autonomous Systems, 142, 2021.<\/p>\n\n\n\n

pdf<\/a> \u2014 code<\/a> \u2014 video<\/a> <\/p>\n<\/div>

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N. Jaquier*, V. Borovitskiy*, A. Smolensky, A. Terenin, T. Asfour, and L. Rozo. “Geometry-aware Bayesian Optimization in Robotics using Riemannian Mat\u00e9rn Kernels<\/strong>“, in Conference on Robot Learning (CoRL), 2021.<\/p>\n\n\n\n

pdf<\/a> \u2014 code<\/a> \u2014 video<\/a> <\/p>\n<\/div><\/div>\n\n\n\n

2020<\/h2>\n\n\n\n
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N. Jaquier. “Robot skills learning with Riemannian manifolds: Leveraging geometry-awareness in robot learning, optimization and control<\/strong>“, PhD thesis, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 2020. 
Nominated for the Asea Brown Boveri Ltd. Award.<\/mark><\/p>\n\n\n\n

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N. Jaquier and L. Rozo. “High-dimensional Bayesian Optimization via Nested Riemannian Manifolds<\/strong>“, in Conference on Neural Information Processing Systems (NeurIPS), 2020.<\/p>\n\n\n\n

pdf<\/a> \u2014 code<\/a> <\/p>\n<\/div><\/div>\n\n\n\n

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N. Jaquier, L. Rozo and S. Calinon. “Analysis and Transfer of Human Movement Manipulability in Industry-like Activities<\/strong>“, in IEEE\/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp.11131-11138, 2020.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & videos<\/a> <\/p>\n<\/div>

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H. Girgin, E. Pignat, N. Jaquier and S. Calinon. “Active Improvement of Control Policies with Bayesian Gaussian Mixture Model<\/strong>“, in IEEE\/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 5395-5401, 2020.<\/p>\n\n\n\n

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2019<\/h2>\n\n\n\n
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N. Jaquier, L. Rozo, S. Calinon and M. B\u00fcrger. “Bayesian Optimization Meets Riemannian Manifolds in Robot Learning<\/strong>“, In Conference on Robot Learning (CoRL), 2019.
Oral presentation, CoRL’19 Best presentation award.<\/mark><\/p>\n\n\n\n

pdf<\/a> \u2014 code & video<\/a> \u2014 presentation<\/a><\/p>\n<\/div>

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N. Jaquier, D. Ginsbourger and S. Calinon. “Learning from demonstration with model-based Gaussian process<\/strong>“, In Conference on Robot Learning (CoRL), 2019.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & video<\/a><\/p>\n<\/div><\/div>\n\n\n\n

2018<\/h2>\n\n\n\n
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N. Jaquier*, L. Rozo*, D. G. Caldwell and S. Calinon. “Geometry-aware Tracking of Manipulability Ellipsoids<\/strong>“, in Robotics: Science and Systems (R:SS), 2018.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & videos<\/a> <\/p>\n<\/div>

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2017<\/h2>\n\n\n\n
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N. Jaquier, M. Connan, C. Castellini and S. Calinon. “Combining electromyography and tactile myography to improve hand and wrist activity detection in prostheses<\/strong>“, Technologies, 5:4, Special issue on assistive robotics.<\/p>\n\n\n\n

pdf<\/a> \u2014 code<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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N. Jaquier and S. Calinon. “Gaussian mixture regression on symmetric positive de\ufb01nite matrices manifolds: Application to wrist motion estimation with sEMG<\/strong>“, in IEEE\/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp.59-64, 2017.<\/p>\n\n\n\n

pdf<\/a><\/p>\n<\/div>

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L. Rozo, N. Jaquier, S. Calinon and D. G. Caldwell. “Learning manipulability ellipsoids for task compatibility in robot manipulation<\/strong>“, in IEEE\/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp.3183-3189, 2017.<\/p>\n\n\n\n

pdf<\/a> \u2014 code & videos<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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N. Jaquier, C. Castellini and S. Calinon. “Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds<\/strong>“, in Myoelectric control (MEC) Symposium, 2017.<\/p>\n\n\n\n

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<\/p>\n","protected":false},"excerpt":{"rendered":"

Preprints S. Kim and N. Jaquier.\u00a0“ChainSplat: A Physics-Inspired Screw-Theoretic Model for Learning Deformable Linear Object Dynamics from Multi-View RGB Videos “, arXiv preprint arXiv:2608.28570, 2026. pdf \u2014 code & video R. P\u00e9rez-Dattari, F. Leiva, A. Testa, L. Rozo, J. Ruiz del Solar, and N. Jaquier. “Let the Dynamics Flow: Stable Flow Matching Dynamical Systems “, arXiv … <\/p>\n

Continue reading “Publications”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-74","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/njaquier.ch\/index.php?rest_route=\/wp\/v2\/pages\/74","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/njaquier.ch\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/njaquier.ch\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/njaquier.ch\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/njaquier.ch\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=74"}],"version-history":[{"count":48,"href":"https:\/\/njaquier.ch\/index.php?rest_route=\/wp\/v2\/pages\/74\/revisions"}],"predecessor-version":[{"id":367,"href":"https:\/\/njaquier.ch\/index.php?rest_route=\/wp\/v2\/pages\/74\/revisions\/367"}],"wp:attachment":[{"href":"https:\/\/njaquier.ch\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=74"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}