Noémie Jaquier | DeepAI

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∙ 07/28/2023

On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds

The generation of energy-efficient and dynamic-aware robot motions that ...

0 Holger Klein, et al. ∙

∙ 10/04/2022

Bringing robotics taxonomies to continuous domains via GPLVM on hyperbolic manifolds

Robotic taxonomies have appeared as high-level hierarchical abstractions...

5 Noémie Jaquier, et al. ∙

∙ 09/30/2022

Riemannian geometry as a unifying theory for robot motion learning and control

Riemannian geometry is a mathematical field which has been the cornersto...

0 Noémie Jaquier, et al. ∙

∙ 09/07/2022

K-VIL: Keypoints-based Visual Imitation Learning

Visual imitation learning provides efficient and intuitive solutions for...

0 Jianfeng Gao, et al. ∙

∙ 08/02/2022

A Riemannian Take on Human Motion Analysis and Retargeting

Dynamic motions of humans and robots are widely driven by posture-depend...

0 Holger Klein, et al. ∙

∙ 06/01/2022

Learning to Sequence and Blend Robot Skills via Differentiable Optimization

In contrast to humans and animals who naturally execute seamless motions...

0 Noémie Jaquier, et al. ∙

∙ 11/02/2021

Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels

Bayesian optimization is a data-efficient technique which can be used fo...

2 Noémie Jaquier, et al. ∙

∙ 10/21/2020

High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds

Despite the recent success of Bayesian optimization (BO) in a variety of...

23 Noémie Jaquier, et al. ∙

∙ 08/06/2020

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Learning from demonstration (LfD) is an intuitive framework allowing non...

0 Hakan Girgin, et al. ∙

∙ 08/04/2020

Analysis and Transfer of Human Movement Manipulability in Industry-like Activities

Humans exhibit outstanding learning, planning and adaptation capabilitie...

0 Noémie Jaquier, et al. ∙

∙ 10/11/2019

Learning from demonstration with model-based Gaussian process

In learning from demonstrations, it is often desirable to adapt the beha...

0 Noémie Jaquier, et al. ∙

∙ 10/11/2019

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Bayesian optimization (BO) recently became popular in robotics to optimi...

0 Noémie Jaquier, et al. ∙

∙ 09/12/2019

Gaussians on Riemannian Manifolds for Robot Learning and Adaptive Control

This paper presents an overview of robot learning and adaptive control a...

0 Sylvain Calinon, et al. ∙

∙ 02/28/2019

Tensor-variate Mixture of Experts

When data are organized in matrices or arrays of higher dimensions (tens...

0 Noémie Jaquier, et al. ∙

∙ 11/27/2018

Geometry-aware Manipulability Transfer

Body posture influences human and robots performance in manipulation tas...

0 Noémie Jaquier, et al. ∙

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