no code implementations • 15 Mar 2024 • Max Braun, Noémie Jaquier, Leonel Rozo, Tamim Asfour
We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies.
no code implementations • 29 Nov 2023 • Noémie Jaquier, Michael C. Welle, Andrej Gams, Kunpeng Yao, Bernardo Fichera, Aude Billard, Aleš Ude, Tamim Asfour, Danica Kragic
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents.
no code implementations • 11 Oct 2023 • Noémie Jaquier, Leonel Rozo, Tamim Asfour
In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data.
no code implementations • 4 Oct 2022 • Noémie Jaquier, Leonel Rozo, Miguel González-Duque, Viacheslav Borovitskiy, Tamim Asfour
This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories.
1 code implementation • 2 Nov 2021 • Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Rozo
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics.
1 code implementation • NeurIPS 2020 • Noémie Jaquier, Leonel Rozo
Despite the recent success of Bayesian optimization (BO) in a variety of applications where sample efficiency is imperative, its performance may be seriously compromised in settings characterized by high-dimensional parameter spaces.
no code implementations • 11 Oct 2019 • Noémie Jaquier, Leonel Rozo, Sylvain Calinon, Mathias Bürger
Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach.
no code implementations • 11 Oct 2019 • Noémie Jaquier, David Ginsbourger, Sylvain Calinon
In learning from demonstrations, it is often desirable to adapt the behavior of the robot as a function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task.
1 code implementation • 28 Feb 2019 • Noémie Jaquier, Robert Haschke, Sylvain Calinon
The proposed formulation takes into account the underlying structure of the data and remains efficient when few training data are available.