Research Assistant


Foto von Jonas Umlauft

M.Sc. Jonas Umlauft

Technische Universität München

Lehrstuhl für Informationstechnische Regelung (Prof. Hirche)

Postadresse

Postal:
Barerstr. 21
80333 München

Short Biography:

Research Interests

  • System identification using machine learning
  • Control based on probabilistic dynamical models
  • Risk-sensitive control using Gaussian Processes

Public Code

  • The code for the publication "Learning Stable Gaussian Process State Space Models" by Jonas Umlauft, Armin Lederer and Sandra Hirche published at the IEEE American Control Conference (ACC) 2018 is available here.
  • The code for the publication "Uncertainty-based Human Trajectory Tracking with Stable Gaussian Process State Space Models" by Lukas Pöhler, Jonas Umlauft and Sandra Hirche published at the IFAC Conference on Cyber-Physical & Human Systems (CPHS) 2018 is available here.
  • The code for the publication "An Uncertainty-based Control Lyapunov Approach for Control-affine Systems Modeled by Gaussian Process" by Jonas Umlauft, Lukas Pöhler and Sandra Hirche published in IEEE Control Systems Letters (L-CSS) and IEEE Conference on Decision and Control (CDC) in 2018 is available here.

Additional materials

Video: Simulation of Stable Gaussian Process based Tracking Control

Video: Robot manipulator with Gaussian Process based Tracking Control

Past Student Projects

  • BA: Risk-Sensitive Cooperative Dynamic Movement Primitives using Gaussian Processes [PDF]
  • BA: Human-Human Cooperation Behaviour Analysis using Uncertainty Models [PDF]
  • BA: Enhancing Variance-Dependent Cooperative Dynamic Movement Primitives [PDF]
  • BA: Learning Stochastic Stable Systems [PDF]
  • HS: Trajectory Tracking of learned Dynamics [PDF]
  • HS: Gaussian Processes for Model Predictive Control [PDF]
  • BA: Online Kinematic Teaching using Optimal Control and Gaussian Processes [PDF]
  • IP: Learning stable human goal directed movements [PDF]
  • HS: Learning on Manifolds for Stable Dynamical System [PDF]
  • FP: Modelling Uncertainties using Wishart Processes [PDF]
  • PP: Learning Stochastic Stable Systems using Sum of Squares Control Lyapunov Functions [PDF]
  • BA: Indirect Adaptive Control based on Gaussian Processes [PDF]
  • BA: Computed Torque Control with Gaussian Process Regression for Robotics [PDF]
  • BA: Learning and Control for Stochastic Stable Systems [PDF]
  • FP: Comparing Multi-Step Predictions for Gaussian Processes [PDF]
  • MA: Data-Driven Approaches to Model Predictive Control [PDF]
  • BA: Path Integral Control for Gaussian Processes [PDF]
  • BA: Efficient Exploration for Gaussian Process Models [PDF]
  • FP: Enhancing Uncertainty-based Control for Gaussian Processes [PDF]
  • BA: Learning Control for Gaussian Process Models [PDF]
  • FP: Efficient Exploration of Dynamical Systems [PDF]
  • BA: Learning Control for Robotic Manipulators [PDF]

Projects

ERC Starting Grant: Control based on Human Models

Publications

2019

  • A. Lederer; J. Umlauft; S. Hirche: Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control. Conference on Neural Information Processing Systems, 2019 more… BibTeX

2018

  • J. Umlauft; L. Pöhler; S. Hirche: An Uncertainty-Based Control Lyapunov Approach for Control-Affine Systems Modeled by Gaussian Process. IEEE Control Systems Letters 2 (3), 2018, 483-488 more… BibTeX
  • J. Umlauft; T. Beckers; S. Hirche: A Scenario-based Optimal Control Approach for Gaussian Process State Space Models. Proceedings of the European Control Conference (ECC), 2018 more… BibTeX
  • L. Pöhler; J. Umlauft; S.Hirche: Uncertainty-based Human Motion Tracking with Stable Gaussian Process State Space Models. IFAC Conference on Cyber-Physical & Human Systems (CPHS), 2018 more… BibTeX
  • T. Beckers; J. Umlauft; S. Hirche: Mean Square Prediction Error of Misspecified Gaussian Process Models. Proceedings of the 57th Conference on Decision and Control (CDC), 2018 more… BibTeX

2017

  • J. Umlauft; A. Lederer; S. Hirche: Learning Stable Gaussian Process State Space Models. American Control Conference (ACC), IEEE, 2017 more… BibTeX
  • J. Umlauft; S. Hirche: Learning Stable Stochastic Nonlinear Dynamical Systems. International Conference on Machine Learning (ICML), 2017 more… BibTeX
  • J. Umlauft; T. Beckers; M. Kimmel; S. Hirche: Feedback Linearization using Gaussian Processes. Proceedings of the Conference on Decision and Control (CDC), IEEE, 2017 more… BibTeX
  • J. Umlauft; Y. Fanger; S. Hirche: Bayesian Uncertainty Modeling for Programming by Demonstration. International Conference on Robotics and Automation (ICRA), IEEE, 2017 more… BibTeX
  • T. Beckers; J. Umlauft; D. Kulić; S. Hirche: Stable Gaussian Process based Tracking Control of Lagrangian Systems. Proceedings of the 56th Conference on Decision and Control (CDC), 2017 more… BibTeX
  • T. Beckers; J. Umlauft; S. Hirche: Stable Model-based Control with Gaussian Process Regression for Robot Manipulators. Proceedings of the 20th IFAC World Congress, 2017 more… BibTeX

2016

  • Y. Fanger; J. Umlauft; S. Hirche: Gaussian Processes for Dynamic Movement Primitives with Application in Knowledge-based Cooperation. International Conference on Intelligent Robots and Systems (IROS), 2016 more… BibTeX

2014

  • J. Umlauft; D. Sieber; S. Hirche: Dynamic Movement Primitives for Cooperative Manipulation and Synchronized Motions. IEEE International Conference on Robotics and Automation (ICRA), 2014, 6 more… BibTeX