I am a postdoctoral researcher at the Department of Electrical and Systems Engineering, University of Pennsylvania.
My research interests include physics-enhanced learning, nonparametric models, and safe learning-based control
In modern control applications such as autonomous driving or human-robot-interaction, the control design and modeling process becomes very time-consuming or even infeasible due to the complexity of the systems. A solution to overcome these issues is provided by data-driven models which require only a minimum of expert knowledge. However, a general problem of data-driven models is the unpredictable outcome which limits their application to non-safety critical systems so far.
My research focuses on safe learning-based modeling and control with Bayesian data-driven models. In particular, I focus on Gaussian Processes (GP) as they provide not only a mean prediction, but also a variance as uncertainty measure of the model. I have developed new GP-based control laws which guarantee safety and high-performance, introduced algorithms to include physical prior knowledge for improved generalization and trustworthiness, and exploited the uncertainty measure for active online learning.
Thomas Beckers is a postdoctoral researcher at the Department of Electrical and Systems Engineering, University of Pennsylvania. He is member of the GRASP Lab and the PRECISE Center. In 2020, he earned his doctorate in Electrical Engineering at the Technical University of Munich (TUM), Germany. He received the B.Sc. and M.Sc. degree in Electrical Engineering in 2010 and 2013, respectively, from the Technical University of Braunschweig, Germany. In 2018, he was a visiting researcher at the University of California, Berkeley. He is a DAAD AInet fellow and was awarded with the Rhode & Schwarz Outstanding Dissertation price. His research interests include physics-enhanced learning, nonparametric models, and safe learning-based control.
04/2022 It is my pleasure to give a keynote talk at the IFAC Symposium on Robot Control, Oct 17-20, 2022 (https://syroco2021.com)
04/2022 I gave a seminar at Vanderbilt University
02/2022 I gave a seminar at Columbia University
02/2022 I have been selected as a DAAD AInet fellow for the Postdoc-NeT-AI 3/2022 – AI and Robotics
12/2021 Our paper entitled "Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics” was accepted for publication at IEEE Control Systems Letters (L-CSS)
11/2021 Our paper entitled "Prediction with Approximated Gaussian Process Dynamical Models" was accepted for publication at IEEE Transaction on Automatic Control
11/2021 I received the Rhode & Schwarz Outstanding Dissertation Award for my work on "Gaussian Process based Modeling and Control with Guarantees"
11/2021 I gave a seminar at the University of Stuttgart
07/2021 We will organize an invited session about "Gaussian Process based Identification and Control" at CDC 2021. Stay tuned for more information!
07/2021 Both of our papers have been accepted for presentation at CDC 2021
05/2021 Our article entitled "Gaussian Process Based Visual Pursuit Control with Unknown Target Motion Learning in Three Dimensions" was accepted for publication in the SICE Journal of Control, Measurement, and System Integration
05/2021 Submission of our article "Online Learning-based Balancing of Feed-forward and Feedback Control"
03/2021 Submission of our articles "Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes" and “Cooperative Visual Pursuit Control with Learning of Position Dependent Target Motion via Gaussian Process”
03/2021 Submission of our article "Safe Trajectory Tracking for Underactuated Vehicles with Partially Unknown Dynamics"
02/2021 Check out my Gaussian Process Regression app
01/2021 Submission of our article "Gaussian Process Based Visual Pursuit Control with Unknown Target Motion Learning in Three Dimensions"
01/2021 I've joined George Pappas' group at University of Pennsylvania as postdoctoral researcher