Search Results for author: Thomas Beckers

Found 15 papers, 1 papers with code

Data-driven Bayesian Control of Port-Hamiltonian Systems

no code implementations9 Sep 2023 Thomas Beckers

Port-Hamiltonian theory is an established way to describe nonlinear physical systems widely used in various fields such as robotics, energy management, and mechanical engineering.

energy management Gaussian Processes +2

Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification

no code implementations15 May 2023 Thomas Beckers, Tom Z. Jiahao, George J. Pappas

Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes.

Gaussian Processes Uncertainty Quantification

Physics-enhanced Gaussian Process Variational Autoencoder

no code implementations15 May 2023 Thomas Beckers, Qirui Wu, George J. Pappas

Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data.

Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior

no code implementations15 May 2023 Thomas Beckers, Jacob Seidman, Paris Perdikaris, George J. Pappas

Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data.

Uncertainty Quantification

Learning Rigidity-based Flocking Control with Gaussian Processes

no code implementations14 Dec 2021 Manuela Gamonal, Thomas Beckers, George J. Pappas, Leonardo J. Colombo

We provide a decentralized control law that exponentially stabilizes the motion of the agents and captures Reynolds boids motion for swarms by using GPs as an online learning-based oracle for the prediction of the unknown dynamics.

Gaussian Processes

Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes

no code implementations31 Mar 2021 Thomas Beckers, Sandra Hirche, Leonardo Colombo

Formation control algorithms for multi-agent systems have gained much attention in the recent years due to the increasing amount of mobile and aerial robotic swarms.

Gaussian Processes

The Impact of Data on the Stability of Learning-Based Control- Extended Version

no code implementations20 Nov 2020 Armin Lederer, Alexandre Capone, Thomas Beckers, Jonas Umlauft, Sandra Hirche

In this paper, we propose a Lyapunov-based measure for quantifying the impact of data on the certifiable control performance.

Gaussian Processes

Deep Learning based Uncertainty Decomposition for Real-time Control

no code implementations6 Oct 2020 Neha Das, Jonas Umlauft, Armin Lederer, Thomas Beckers, Sandra Hirche

Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration.

Efficient Exploration

Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics

no code implementations14 Sep 2020 Thomas Beckers, Leonardo Colombo, Sandra Hirche, George J. Pappas

To overcome this issue, we present a tracking control law for underactuated rigid-body dynamics using an online learning-based oracle for the prediction of the unknown dynamics.

Prediction with Approximated Gaussian Process Dynamical Models

no code implementations25 Jun 2020 Thomas Beckers, Sandra Hirche

The modeling and simulation of dynamical systems is a necessary step for many control approaches.

Smart Forgetting for Safe Online Learning with Gaussian Processes

no code implementations L4DC 2020 Jonas Umlauft, Thomas Beckers, Alexandre Capone, Armin Lederer, Sandra Hirche

The identification of unknown dynamical systems using supervised learning enables model-based control of systems that cannot be modeled based on first principles.

Computational Efficiency Gaussian Processes

Localized active learning of Gaussian process state space models

no code implementations L4DC 2020 Alexandre Capone, Jonas Umlauft, Thomas Beckers, Armin Lederer, Sandra Hirche

We apply the proposed method to explore the state space of various dynamical systems and compare our approach to a commonly used entropy-based exploration strategy.

Active Learning Model Predictive Control

Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization

no code implementations12 Sep 2019 Thomas Beckers, Somil Bansal, Claire J. Tomlin, Sandra Hirche

In this work, we present a framework to optimize the kernel and hyperparameters of a kernel-based model directly with respect to the closed-loop performance of the model.

Bayesian Optimization Model Selection

Mean Square Prediction Error of Misspecified Gaussian Process Models

no code implementations16 Nov 2018 Thomas Beckers, Jonas Umlauft, Sandra Hirche

A naturally provided model confidence is highly relevant for system-theoretical considerations to provide guarantees for application scenarios.

regression valid

Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems

1 code implementation19 Jun 2018 Thomas Beckers, Dana Kulić, Sandra Hirche

The model fidelity is used to adapt the feedback gains allowing low feedback gains in state space regions of high model confidence.

Cannot find the paper you are looking for? You can Submit a new open access paper.