Publications

Journal articles

Jacob Goodman,  Thomas Beckers, and Leonardo J. Colombo. “Geometric Control for Load Transportation with Quadrotor UAVs by Elastic Cables”.   In: IEEE Transactions on Control Systems Technology. 2023. doi: 10.1109/TCST.2023.3296730.

Thomas Beckers, Leonardo J. Colombo, and Sandra Hirche. “Safe Trajectory Tracking for Underactuated Vehicles with Partially Unknown Dynamics”.  In: Journal of Geometric Mechanics. 2022. doi: 10.3934/jgm.2022018.

Thomas Beckers, Leonardo J. Colombo, Sandra Hirche, and George J. Pappas. “Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics”.
In: IEEE Control Systems Letters (L-CSS). 2022. doi: 10.1109/LCSYS.2021.3138546. [Preprint] 

Thomas Beckers and Sandra Hirche. “Prediction with Approximated Gaussian Process Dynamical Models”.
In: Transaction on Automatic Control. 2022. doi: 10.1109/TAC.2021.3131988. [Preprint] [bibtex]

M. Omainska, J. Yamauchi, Thomas Beckers, T. Hatanaka, Sandra Hirche, and M. Fujita. “Gaussian Process Based Visual Pursuit Control with Unknown Target Motion Learning in Three Dimensions”.
In: SICE Journal of Control, Measurement, and System Integration. 2021. doi: 10.1080/18824889.2021.1936855

Thomas Beckers, D. Kulić, and Sandra Hirche. “Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems”.
In: Automatica 103 (2019), pp. 390–397. doi: 10.1016/j.automatica.2019.01.023. [Preprint] [bibtex] [Video]

Conference paper

Kaiyuan Tan, Peilun Li, Jun Wang, Thomas Beckers. “PnP-PIML: Physics-informed Learning of Outlier Dynamics using Uncertainty Quantified Port-Hamiltonian Models”. In: Proceedings of the 2025 IEEE Conference on Robotics and Automation (ICRA). 2025. (accepted)

Xia Wang, Yuwei Yang, Yifan Shangguan, Weiyu Yan, Ziyan An, Matthew Bunting, Matthew Nice, Thomas Beckers, Meiyi Ma, Dan Work, and Jonathan Sprinkle. “Interpretable Finite State Machine Controller: A Case Study on Lane Merge Yield Mode”.
In: Proceedings of the 27th IEEE International Conference on Intelligent Transportation Systems (ITSC). 2024. 

Kaiyuan Tan, Peilun Li, and Thomas Beckers. “Physics-Constrained Learning of PDE Systems with Uncertainty Quantified Port-Hamiltonian Models”.
In: Proceedings of the 5th Conference on Learning for Dynamics and Control. 2024. [Paper]

Peilun Li, Kaiyuan Tan, and Thomas Beckers. “PyGpPHs: A Python Package for Bayesian Modeling of Port-Hamiltonian Systems”.
In: Proceedings of the 8th IFAC Workshop on Lagrangian and Hamiltonian Methods for Non Linear Control. 2024. [Toolbox]

Thomas Beckers. “Data-driven Bayesian Control of Port-Hamiltonian Systems”.
In: Proceedings of the Conference on Decision and Control. 2023. doi: 10.1109/CDC49753.2023.10384219. [Preprint] 

Neha Das, Jonas Umlauft, Armin Lederer, Alexandre Capone, Thomas Beckers, and Sandra Hirche. “Deep Learning based Uncertainty Decomposition for Real-time Control”. In: Proceedings of the IFAC World Congress. 2023. doi: 10.1016/j.ifacol.2023.10.1671.

Thomas Beckers, Tom Zhang Jiahao, and George J. Pappas. “Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification”.
In: Proceedings of the IFAC World Congress. 2023. doi: 10.1016/j.ifacol.2023.10.1621. [Preprint] 

Thomas Beckers, Qirui Wu, and George J. Pappas. “Physics-enhanced Gaussian Process Variational Autoencoder”.
In: Proceedings of the 5th Conference on Learning for Dynamics and Control. 2023. [paper] [poster]

Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, and George J. Pappas. “Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior”.
In: Proceedings of the Conference on Decision and Control. 2022. doi: 10.1109/CDC51059.2022.9992733.  [Preprint] [bibtex] [video]

Thomas Beckers, Leonardo J. Colombo, M. Morari, and George J. Pappas. “Learning-based Balancing of Model-based and Feedback Control for Second-order Mechanical Systems”.
In: Proceedings of the Conference on Decision and Control. 2022. doi: 10.1109/CDC51059.2022.9992751. [bibtex] [video]

Thomas Beckers, George J. Pappas, Leonardo J. Colombo. “Learning Rigidity-based Flocking Control using Gaussian Processes with Probabilistic Stability Guarantees”.
In: Proceedings of the Conference on Decision and Control (accepted). 2022. doi: 10.1109/CDC51059.2022.9992465. [Preprint] [bibtex] [video]

Thomas Beckers, Sandra Hirche, and Leonardo J. Colombo. “Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes”.
In: Proceedings of the Conference on Decision and Control. 2021. doi: 10.1109/CDC45484.2021.9683423. [Preprint] [Video]

Junya Yamauchi, Marco Omainska, Thomas Beckers, Takeshi Hatanaka, Sandra Hirche, and Masayuki Fujita. “Cooperative Visual Pursuit Control with Learning of Position Dependent Target Motion via Gaussian Process”.
In: Proceedings of the Conference on Decision and Control. 2021. doi: 10.1109/CDC45484.2021.9683432.

Armin Lederer, Alexandre Capone, Thomas Beckers, Jonas Umlauft, and Sandra Hirche. “The Impact of Data on the Stability of Learning-Based Control”.
In: Proceedings of the 3rd Conference on Learning for Dynamics and Control. 2021. [Preprint] [bibtex]

Junya Yamauchi, Thomas Beckers, Marco Omainska, Takeshi Hatanaka, Sandra Hirche,  and Masayuki Fujita. “Visual Pursuit Control with Target Motion Learning via Gaussian Process”.
In: Proceedings of the Conference of the Society of Instrument and Control Engineers of Japan. 2020. doi: 10.23919/SICE48898.2020.9240221. [bibtex]
(Finalist of SICE Annual Conference International Award)

Jonas Umlauft, Thomas Beckers, Alexandre Capone, Armin Lederer, and Sandra Hirche. “Smart Forgetting for Safe Online Learning with Gaussian Processes”.
In: Proceedings of the 2nd Conference on Learning for Dynamics and Control. 2020. [Preprint] [bibtex] [Video]

Alexandre Capone, Gerrit Noske, Jonas Umlauft, Thomas Beckers, Armin Lederer, and Sandra Hirche. “Localized Active Learning of Gaussian Process State Space Models”.
In: Proceedings of the 2nd Conference on Learning for Dynamics and Control. 2020. [Preprint] [bibtex] [Video]

Thomas Beckers,  Somil Bansal, Claire J. Tomlin, and Sandra Hirche. “Closed-loop Model Selection for Kernel based Models via Bayesian Optimization”.
In: Proceedings of the Conference on Decision and Control. 2019. doi: 10.1109/CDC40024.2019.9029690. [Preprint] [bibtex]

Thomas Beckers, and Sandra Hirche. “Keep Soft Robots Soft - a Data-driven based Trade-off between Feed-forward and Feedback Control”.
In: Robotics: Science and Systems, Workshop on Robust autonomy: tools for safety in real-world uncertain environments. 2019. [Preprint] [bibtex] [Video]

Thomas Beckers and Sandra Hirche. “Gaussian Process based Passivation of a Class of Nonlinear Systems with Unknown Dynamics”.
In: Proceedings of the European Control Conference. 2018. doi: 10.23919/ECC.2018.8550311. [Preprint] [bibtex]

Thomas Beckers, Jonas Umlauft, and Sandra Hirche. “Mean Square Prediction Error of Misspecified Gaussian Process State Space Models”.
In: Proceedings of the Conference on Decision and Control. 2018. doi: 10.1109/CDC.2018.8619163. [Preprint] [bibtex]

Jonas Umlauft, Thomas Beckers, and Sandra Hirche. “A Scenario-based Optimal Control Approach for Gaussian Process State Space Models”.
In: Proceedings of the European Control Conference. 2018. doi: 10.23919/ECC.2018.8550458. [Preprint] [bibtex]

Thomas Beckers, Jonas Umlauft, and Sandra Hirche. “Stable Model-based Control with Gaussian Process Regression for Robot Manipulators”.
In: Proceedings of the IFAC World Congress. 2017. doi: 10.1016/j.ifacol.2017.08.359. [Preprint] [bibtex]

Thomas Beckers, Jonas Umlauft, D. Kulić, and Sandra Hirche. “Stable Gaussian Process based Tracking Control of Lagrangian Systems”.
In: Proceedings of the Conference on Decision and Control. 2017.doi: 10.1109/CDC.2017.8264427. [Preprint] [bibtex] [Video]

Jonas Umlauft, Thomas Beckers, Melanie Kimmel, and Sandra Hirche. “Feedback Linearization using Gaussian Processes”.
In: Proceedings of the Conference on Decision and Control. IEEE, 2017. doi: 10.1109/CDC.2017.8264435. [Preprint] [bibtex]

Thomas Beckers and Sandra Hirche. “Equilibrium Distributions and Stability Analysis of Gaussian Process State Space Models”.
In: Proceedings of the Conference on Decision and Control. 2016. doi: 10.1109/CDC.2016.7799247. [Preprint] [bibtex]

Thomas Beckers and Sandra Hirche. “Stability of Gaussian Process State Space Models”.
In: Proceedings of the European Control Conference. 2016. doi: 10.1109/ECC.2016.7810630. [Preprint] [bibtex]

Robert Geise, Jen Schueuer, Lena Thiele, Kai Notte, Thomas Beckers, and Achim Enders. “A Slotted Waveguide Setup as Scaled Instrument-Landing-System for Measuring Scattering of an A380 and Large Objects”.
In: Proceedings of the European Conference on Antennas and Propagation. 2010. isbn: 978-84-7653-472-4. [bibtex]

Miscellaneous articles

Jonas Umlauft, Armin Lederer, Thomas Beckers, and Sandra Hirche. “Real-time Uncertainty Decomposition for Online Learning Control”.
arXiv: 2010.02613 [cs.LG]. 2020. [Preprint]

Thomas Beckers. “Gaussian Process based Modeling and Control with Guarantees ”.
Doctoral Dissertation. Technical University of Munich. 2021. [Full text] [bibtex]

Thomas Beckers. “An Introduction to Gaussian Process Models”.
arXiv: 2102.05497 [eess.SY]. 2021. [Full text]

Ken Friedl, W. Zhou, Markus Rank, Armin Ergin, Thomas Beckers, A. Mahdizadeh, Angelika Peer, Sandra Hirche. “Haptik von Bedienelementen und Interieurkomponenten im Fahrzeug”.
In: DFG Report SFB453 T5. 2014.  [bibtex]