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

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. (accepted)

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]