Jonas Umlauft

CoRR, 2023

2021

How Training Data Impacts Performance in Learning-Based Control.

[BibT_eX]

[DOI]

IEEE Control. Syst. Lett., 2021

Data Selection for Multi-Task Learning Under Dynamic Constraints.

[BibT_eX]

[DOI]

IEEE Control. Syst. Lett., 2021

Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control.

[BibT_eX]

[DOI]

CoRR, 2021

The Impact of Data on the Stability of Learning-Based Control.

[BibT_eX]

[DOI]

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Gaussian Process-Based Real-Time Learning for Safety Critical Applications.

[BibT_eX]

[DOI]

Alejandro Jose Ordóñez Conejo

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Feedback Linearization Based on Gaussian Processes With Event-Triggered Online Learning.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2020

Learning stochastically stable Gaussian process state-space models.

[BibT_eX]

[DOI]

IFAC J. Syst. Control., 2020

The Value of Data in Learning-Based Control for Training Subset Selection.

[BibT_eX]

[DOI]

CoRR, 2020

Real-time Uncertainty Decomposition for Online Learning Control.

[BibT_eX]

[DOI]

CoRR, 2020

Localized active learning of Gaussian process state space models.

[BibT_eX]

[DOI]

CoRR, 2020

Smart Forgetting for Safe Online Learning with Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Localized active learning of Gaussian process state space models.

[BibT_eX]

[DOI]

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

2019

Correction to "An Uncertainty-Based Control Lyapunov Approach for Control-Affine Systems Modeled by Gaussian Process".

[BibT_eX]

[DOI]

Lukas Pöhler

IEEE Control. Syst. Lett., 2019

Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves.

[BibT_eX]

[DOI]

CoRR, 2019

Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

An Uncertainty-Based Control Lyapunov Approach for Control-Affine Systems Modeled by Gaussian Process.

[BibT_eX]

[DOI]

Lukas Pöhler

IEEE Control. Syst. Lett., 2018

Stable Model-based Control with Gaussian Process Regression for Robot Manipulators.

[BibT_eX]

[DOI]

Thomas Beckers

CoRR, 2018

Scenario-based Optimal Control for Gaussian Process State Space Models.

[BibT_eX]

[DOI]

Thomas Beckers

Proceedings of the 16th European Control Conference, 2018

Mean Square Prediction Error of Misspecified Gaussian Process Models.

[BibT_eX]

[DOI]

Thomas Beckers

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Bayesian uncertainty modeling for programming by demonstration.

[BibT_eX]

[DOI]

Yunis Fanger

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Learning Stable Stochastic Nonlinear Dynamical Systems.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

Feedback linearization using Gaussian processes.

[BibT_eX]

[DOI]

Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

Stable Gaussian process based tracking control of Lagrangian systems.

[BibT_eX]

[DOI]

Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

Learning stable Gaussian process state space models.

[BibT_eX]

[DOI]

Proceedings of the 2017 American Control Conference, 2017

2016

Gaussian processes for dynamic movement primitives with application in knowledge-based cooperation.

[BibT_eX]

[DOI]

Yunis Fanger

Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

2014

Dynamic Movement Primitives for cooperative manipulation and synchronized motions.

[BibT_eX]

[DOI]

Dominik Sieber