152 citations · 285 across the 20 of their papers we have counts for
21 papers
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification
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. The dy…
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
Thomas Beckers, Jacob Seidman, Paris Perdikaris +1
Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which…
Physics-enhanced Gaussian Process Variational Autoencoder
Thomas Beckers, Qirui Wu, George J. Pappas
Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data. Using video clips as input data, the encoder may be used to de…
Learning Rigidity-based Flocking Control with Gaussian Processes
Manuela Gamonal, Thomas Beckers, George J. Pappas +1
Flocking control of multi-agents system is challenging for agents with partially unknown dynamics. This paper proposes an online learning-based controller to stabilize flocking mot…
Geometric Control for Load Transportation with Quadrotor UAVs by Elastic Cables
Jacob R. Goodman, Juan S. Cely, Thomas Beckers +1
Groups of unmanned aerial vehicles (UAVs) are increasingly utilized in transportation task as the combined strength allows to increase the maximum payload. However, the resulting m…
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes
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. The design of…
An Introduction to Gaussian Process Models
Thomas Beckers
Within the past two decades, Gaussian process regression has been increasingly used for modeling dynamical systems due to some beneficial properties such as the bias variance trade…
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…
Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics
Thomas Beckers, Leonardo Colombo, Sandra Hirche +1
Underactuated vehicles have gained much attention in the recent years due to the increasing amount of aerial and underwater vehicles as well as nanosatellites. Trajectory tracking…
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…
Prediction with Approximated Gaussian Process Dynamical Models
Thomas Beckers, Sandra Hirche
The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g.,…
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization
Thomas Beckers, Somil Bansal, Claire J. Tomlin +1
Kernel-based nonparametric models have become very attractive for model-based control approaches for nonlinear systems. However, the selection of the kernel and its hyperparameters…
Keep soft robots soft -- a data-driven based trade-off between feed-forward and feedback control
Thomas Beckers, Sandra Hirche
Tracking control for soft robots is challenging due to uncertainties in the system model and environment. Using high feedback gains to overcome this issue results in an increasing…
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…
Equilibrium Distributions and Stability Analysis of Gaussian Process State Space Models
Thomas Beckers, Sandra Hirche
Gaussian Process State Space Models (GP-SSM) are a data-driven stochastic model class suitable to represent nonlinear dynamics. They have become increasingly popular in non-paramet…
Gaussian Process based Passivation of a Class of Nonlinear Systems with Unknown Dynamics
Thomas Beckers, Sandra Hirche
The paper addresses the problem of passivation of a class of nonlinear systems where the dynamics are unknown. For this purpose, we use the highly flexible, data-driven Gaussian pr…
Stability of Gaussian Process State Space Models
Thomas Beckers, Sandra Hirche
Gaussian Process State Space Models (GP-SSMs) are a non-parametric model class suitable to represent nonlinear dynamics. They become increasingly popular in data-driven modeling ap…
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-…
Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems
Thomas Beckers, Dana Kulić, Sandra Hirche
Perfect tracking control for real-world Euler-Lagrange systems is challenging due to uncertainties in the system model and external disturbances. The magnitude of the tracking erro…