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High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
Despite the recent success of Bayesian optimization (BO) in a variety of...
10/21/2020 ∙ by Noémie Jaquier, et al. ∙
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Active Improvement of Control Policies with Bayesian Gaussian Mixture Model
Learning from demonstration (LfD) is an intuitive framework allowing non...
08/06/2020 ∙ by Hakan Girgin, et al. ∙
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Analysis and Transfer of Human Movement Manipulability in Industry-like Activities
Humans exhibit outstanding learning, planning and adaptation capabilitie...
08/04/2020 ∙ by Noémie Jaquier, et al. ∙
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Learning from demonstration with model-based Gaussian process
In learning from demonstrations, it is often desirable to adapt the beha...
10/11/2019 ∙ by Noémie Jaquier, et al. ∙
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Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
Bayesian optimization (BO) recently became popular in robotics to optimi...
10/11/2019 ∙ by Noémie Jaquier, et al. ∙
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Gaussians on Riemannian Manifolds for Robot Learning and Adaptive Control
This paper presents an overview of robot learning and adaptive control a...
09/12/2019 ∙ by Sylvain Calinon, et al. ∙
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Tensor-variate Mixture of Experts
When data are organized in matrices or arrays of higher dimensions (tens...
02/28/2019 ∙ by Noémie Jaquier, et al. ∙
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Geometry-aware Manipulability Transfer
Body posture influences human and robots performance in manipulation tas...
11/27/2018 ∙ by Noémie Jaquier, et al. ∙
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