Noémie Jaquier

Christian R. G. Dreher

André Meixner

Franziska Krebs

CoRR, 2023

Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges.

[BibT_eX]

[DOI]

CoRR, 2023

Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot Learning.

[BibT_eX]

[DOI]

Leonel Rozo

CoRR, 2023

An Evaluation of Action Segmentation Algorithms on Bimanual Manipulation Datasets.

[BibT_eX]

[DOI]

IROS, 2023

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

[BibT_eX]

[DOI]

IROS, 2023

2022

Learning to Sequence and Blend Robot Skills via Differentiable Optimization.

[BibT_eX]

[DOI]

IEEE Robotics Autom. Lett., 2022

Bringing robotics taxonomies to continuous domains via GPLVM on hyperbolic manifolds.

[BibT_eX]

[DOI]

Leonel Dario Rozo

Miguel González Duque

Viacheslav Borovitskiy

CoRR, 2022

Riemannian Geometry as a Unifying Theory for Robot Motion Learning and Control.

[BibT_eX]

[DOI]

Proceedings of the Robotics Research, 2022

A Riemannian Take on Human Motion Analysis and Retargeting.

[BibT_eX]

[DOI]

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

2021

Tensor-variate mixture of experts for proportional myographic control of a robotic hand.

[BibT_eX]

[DOI]

Robert Haschke

Robotics Auton. Syst., 2021

Geometry-aware manipulability learning, tracking, and transfer.

[BibT_eX]

[DOI]

Int. J. Robotics Res., 2021

Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels.

[BibT_eX]

[DOI]

Viacheslav Borovitskiy

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2020

High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds.

[BibT_eX]

[DOI]

Leonel Dario Rozo

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

[BibT_eX]

[DOI]

Leonel Dario Rozo

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model.

[BibT_eX]

[DOI]

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

2019

Gaussians on Riemannian Manifolds for Robot Learning and Adaptive Control.

[BibT_eX]

[DOI]

CoRR, 2019

Tensor-variate Mixture of Experts.

[BibT_eX]

[DOI]

Robert Haschke

CoRR, 2019

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning.

[BibT_eX]

[DOI]

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

Learning from demonstration with model-based Gaussian process.

[BibT_eX]

[DOI]

David Ginsbourger

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

2018

Geometry-aware Manipulability Transfer.

[BibT_eX]

[DOI]

CoRR, 2018

Geometry-aware Tracking of Manipulability Ellipsoids.

[BibT_eX]

[DOI]

Proceedings of the Robotics: Science and Systems XIV, 2018

2017

Learning manipulability ellipsoids for task compatibility in robot manipulation.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017

Gaussian mixture regression on symmetric positive definite matrices manifolds: Application to wrist motion estimation with sEMG.

[BibT_eX]

[DOI]