21 citations · 69 across the 10 of their papers we have counts for
13 papers
Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors
Armin Lederer, Jonas Umlauft, Sandra Hirche
Due to the increasing complexity of technical systems, accurate first principle models can often not be obtained. Supervised machine learning can mitigate this issue by inferring m…
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
In application areas where data generation is expensive, Gaussian processes are a preferred supervised learning model due to their high data-efficiency. Particularly in model-based…
The Impact of Data on the Stability of Learning-Based Control- Extended Version
Armin Lederer, Alexandre Capone, Thomas Beckers +2
Despite the existence of formal guarantees for learning-based control approaches, the relationship between data and control performance is still poorly understood. In this paper, w…
Deep Learning based Uncertainty Decomposition for Real-time Control
Neha Das, Jonas Umlauft, Armin Lederer +2
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty…
Localized active learning of Gaussian process state space models
Alexandre Capone, Jonas Umlauft, Thomas Beckers +2
The performance of learning-based control techniques crucially depends on how effectively the system is explored. While most exploration techniques aim to achieve a globally accura…
How Training Data Impacts Performance in Learning-based Control
Armin Lederer, Alexandre Capone, Jonas Umlauft +1
When first principle models cannot be derived due to the complexity of the real system, data-driven methods allow us to build models from system observations. As these models are e…
Data selection for multi-task learning under dynamic constraints
Alexandre Capone, Armin Lederer, Jonas Umlauft +1
Learning-based techniques are increasingly effective at controlling complex systems using data-driven models. However, most work done so far has focused on learning individual task…
Feedback Linearization based on Gaussian Processes with event-triggered Online Learning
Jonas Umlauft, Sandra Hirche
Combining control engineering with nonparametric modeling techniques from machine learning allows to control systems without analytic description using data-driven models. Most exi…
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves
Armin Lederer, Jonas Umlauft, Sandra Hirche
The posterior variance of Gaussian processes is a valuable measure of the learning error which is exploited in various applications such as safe reinforcement learning and control…
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
Data-driven models are subject to model errors due to limited and noisy training data. Key to the application of such models in safety-critical domains is the quantification of the…
Stable Gaussian Process based Tracking Control of Lagrangian Systems
Thomas Beckers, Jonas Umlauft, Dana Kulić +1
High performance tracking control can only be achieved if a good model of the dynamics is available. However, such a model is often difficult to obtain from first order physics onl…
Stable Model-based Control with Gaussian Process Regression for Robot Manipulators
Thomas Beckers, Jonas Umlauft, Sandra Hirche
Computed-torque control requires a very precise dynamical model of the robot for compensating the manipulator dynamics. This allows reduction of the controller's feedback gains res…
Mean Square Prediction Error of Misspecified Gaussian Process Models
Thomas Beckers, Jonas Umlauft, Sandra Hirche
Nonparametric modeling approaches show very promising results in the area of system identification and control. A naturally provided model confidence is highly relevant for system-…