Thomas Beckers' Lab
Research
The modeling and control of modern applications such as autonomous driving or human-robot interaction can be excessively time-consuming or even unfeasible due to the growing complexity of technical systems. To address these challenges, data-driven models are a viable solution that requires minimal expert knowledge and have shown remarkable results. However, a major drawback of these models is their unpredictable outcomes, which limits their applicability to non-safety critical systems.
Our research focuses on safe learning-based modeling and control of physical systems. We are working on developing novel data-driven control methods that ensure both safe operation and high-performance for the closed-loop system. In addition, we are exploring algorithms that incorporate physical prior knowledge into learning-based models to enhance their generalizability, reliability and interpretability. The research outcomes contribute to the development of safe, robust, and intelligent control methods for physical systems.
News
11/2023 I will present our most recent work on Gaussian Process Port-Hamilonian Systems at the Second Workshop on Physics Enhancing Machine Learning in Applied Mechanics [slides]
08/2023 Vanderbilt University is supporting my work on data-driven control for soft robots with a RAMP award
08/2023 Peilun presented a poster at the 25th Anniversary of the Institut of Software Integrated Systems
07/2023 I gave a seminar on "Physics-enhanced Gaussian Processes for Learning of Electromechanical Systems" at the DDPS webinar series. Check out the recording.
06/2023 I will present our poster on "Physics-enhanced Gaussian Process Variational Autoencoder" at L4DC in Philadelphia
05/2023 The Chambers Family Summer Research Fund will support Peilun's summer research in the School of Engineering
04/2023 If you are interested in Gaussian Process based control, please join our workshop at IFAC WC
03/2023 I will present our most recent work on Gaussian Process Port-Hamilonian Systems at the Artificial Intelligence for Robust Engineering and Science (AIRES) workshop
03/2023 Our paper "Physics-enhanced Gaussian Process Variational Autoencoder" has been accepted at L4DC 2023.
03/2023 Two accepted paper at IFAC WC. In addition, we will organize a workshop on Gaussian process based identification and Control
03/2023 I gave a seminar on Gaussian Process Port-Hamiltonian systems at University of Wuppertal
01/2023 I offer a project at the Vanderbilt School of Engineering Summer Research Program for undergraduates.
01/2023 I've started my new position as Assistant Professor of Computer Science at Vanderbilt University.
12/2022 I presented our three papers on Gaussian process based learning and control at the IEEE Conference on Decision and Control
10/2022 It was my pleasure to give a keynote at the IFAC Symposium on Robot Control, Oct 17-20, 2022
09/2022 We will organize a workshop on Gaussian Process Learning-based Control at the IEEE Conference on Decision and Control (CDC) 2022
07/2022 All of our three papers have been accepted at CDC 2022. See you there!
06/2022 Our article "Safe Trajectory Tracking for Underactuated Vehicles with Partially Unknown Dynamics” has been accepted for publication in the Journal of Geometric Mechanics.
06/2022 I am happy to share that I will be joining the Department of Computer Science at Vanderbilt University as Assistant Professor in January 2023.
05/2022 I gave a seminar at Technical University of Darmstadt, German Aerospace Center (DLR), and Leibniz University Hannover
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