20 citations · 32 across the 6 of their papers we have counts for
7 papers
Riemannian geometry as a unifying theory for robot motion learning and control
Noémie Jaquier, Tamim Asfour
Riemannian geometry is a mathematical field which has been the cornerstone of revolutionary scientific discoveries such as the theory of general relativity. Despite early uses in r…
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
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 setting…
Active Improvement of Control Policies with Bayesian Gaussian Mixture Model
Hakan Girgin, Emmanuel Pignat, Noémie Jaquier +1
Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a grea…
Analysis and Transfer of Human Movement Manipulability in Industry-like Activities
Noémie Jaquier, Leonel Rozo, Sylvain Calinon
Humans exhibit outstanding learning, planning and adaptation capabilities while performing different types of industrial tasks. Given some knowledge about the task requirements, hu…
Learning from demonstration with model-based Gaussian process
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…
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
Noémie Jaquier, Leonel Rozo, Sylvain Calinon +1
Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency an…