{"id":11,"date":"2024-10-29T21:30:37","date_gmt":"2024-10-29T21:30:37","guid":{"rendered":"https:\/\/njaquier.ch\/?page_id=11"},"modified":"2026-09-05T07:58:22","modified_gmt":"2026-09-05T07:58:22","slug":"about","status":"publish","type":"page","link":"https:\/\/njaquier.ch\/?page_id=11","title":{"rendered":"GeoRob Lab"},"content":{"rendered":"\n
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Welcome to the Geometric Robot Learning Lab! <\/p>\n\n\n\n

In the GeoRob Lab, we develop data-efficient robot learning, optimization, and control algorithms with sound theoretical guarantees. To do so, we follow an interdisciplinary approach that considers inductive bias, typically in the form of geometry and physics, as cornerstone.<\/p>\n<\/div><\/div>\n\n\n\n

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PhD students<\/h2>\n\n\n\n
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Loizos Hadjiloizou<\/strong><\/p>\n\n\n\n

Geometry and symmetries of operational space learning and control<\/p>\n<\/div>\n\n\n\n

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Riccardo Morandi<\/strong><\/p>\n\n\n\n

Geometries of deformable object manipulation<\/p>\n<\/div>\n\n\n\n

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Katharina Friedl<\/strong><\/p>\n\n\n\n

Physics-informed geometric learning and control for dynamical systems<\/p>\n\n\n\n

Main supervisor: Danica Kragic<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

Postdocs<\/h2>\n\n\n\n
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Rodrigo P\u00e9rez Dattari<\/a><\/strong><\/p>\n\n\n\n

Generalizable imitation learning via inductive biases<\/p>\n<\/div>\n\n\n\n

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Seungyeon Kim<\/a><\/strong><\/p>\n\n\n\n

Vision-based geometric robot learning for manipulation<\/p>\n<\/div>\n\n\n\n

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External PhD students<\/h2>\n\n\n\n
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Bent Speksnijder<\/strong><\/p>\n\n\n\n

Machine learning for environmental chemistry<\/p>\n\n\n\n

Main supervisor: Oksana Golovko<\/a>, SLU<\/p>\n<\/div>\n\n\n\n

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Research Engineers<\/h2>\n\n\n\n
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Ulises Campod\u00f3nico<\/strong><\/p>\n\n\n\n

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Mart\u00edn Gallegos<\/strong><\/p>\n<\/div>\n\n\n\n

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Alumnis<\/h2>\n\n\n\n
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Federico Pavesi<\/strong><\/p>\n\n\n\n

Bayesian optimization on Riemannian manifolds<\/p>\n\n\n\n

Visiting PhD student, University of Milano-Bicocca<\/p>\n<\/div>\n\n\n\n

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Welcome to the Geometric Robot Learning Lab! In the GeoRob Lab, we develop data-efficient robot learning, optimization, and control algorithms with sound theoretical guarantees. To do so, we follow an interdisciplinary approach that considers inductive bias, typically in the form of geometry and physics, as cornerstone. PhD students Loizos Hadjiloizou Geometry and symmetries of operational … <\/p>\n

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