Thomas Beckers' Lab


The modeling and control of modern applications such as autonomous driving, soft robotics 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.



07/2024 We organized a workshop on physics-informed learning with fantastic talks and a great tutorial session at the American Control Conference (ACC).

06/2024 Keeping strong ties with the European control community by presenting our recent work at the ECC Workshop on Physics-Informed Learning in Control

05/2024 I gave a presentation at the Artificial Intelligence for Robust Engineering & Science Workshop (AIRES), PNNL

04/2024 I gave a virtual presentation at the UNC Charlotte Robotics and Controls Seminar series

04/2024 Peilun Li and Kaiyuan Tan got a paper accepted at the 8th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control.

02/2024 Peilun Li and Kaiyuan Tan will present their most recent works on physics-informed learning at the 16th World Congress on Computational Mechanics

01/2024 We will organize a workshop on Physics-informed Machine Learning for Modeling, Control, and Optimization at the American Control Conference, July 10-12, 2024.

12/2023 Our workshop on "Physics-informed Learning for Control and Optimization" at the CDC 2023 was a great success with more than 80 registered participants, making it one of the largest workshops.

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.