31 citations · 70 across the 31 of their papers we have counts for
3 papers · 1 filter
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…
Tensor-variate Mixture of Experts for Proportional Myographic Control of a Robotic Hand
Noémie Jaquier, Robert Haschke, Sylvain Calinon
When data are organized in matrices or arrays of higher dimensions (tensors), classical regression methods first transform these data into vectors, therefore ignoring the underlyin…