Publications

Under review

T. Beckers, S. Hirche, and L. Colombo. Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes”.
arXiv: 2104.00130 [eess.SY]. 2021. [Preprint]

J. Yamauchi, M. Omainska, T. Beckers, T. Hatanaka, S. Hirche, M. Fujita. “Cooperative Visual Pursuit Control with Learning of Position Dependent Target Motion via Gaussian Process”. 2021.

J. Umlauft, A. Lederer, T. Beckers, and S. Hirche. “Real-time Uncertainty Decomposition for Online Learning Control”.
arXiv: 2010.02613 [cs.LG]. 2020. [Preprint]

T. Beckers, L. Colombo, and S. Hirche. “Safe Learning-based Trajectory Tracking for Underactuated Vehicles with Partially Unknown Dynamics”.
arXiv: 2009.06689 [eess.SY]. 2020. [Preprint]

T. Beckers and S. Hirche. Prediction with Approximated Gaussian Process Dynamical Models”.
arXiv: 2006.14551 [eess.SY]. 2020. [Preprint] [bibtex]


Peer-reviewed articles

M. Omainska, J. Yamauchi, T. Beckers, T. Hatanaka, S. Hirche, 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.

A. Lederer, A. Capone, T. Beckers, J. Umlauft, and S. Hirche. “The Value of Data in Learning-Based Control for Training Subset Selection”.
In: Learning for Dynamics and Control. Ed. by Proceedings of Machine Learning Research. 2021. [Preprint]

J. Yamauchi, T. Beckers, M. Omainska, T. Hatanaka, S. Hirche, M. 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)

J. Umlauft, T. Beckers, A. Capone, A. Lederer, and S. Hirche. “Smart Forgetting for Safe Online Learning with Gaussian Processes”.
In: Learning for Dynamics and Control. Ed. by Proceedings of Machine Learning Research. 2020. [Preprint] [bibtex]

A. Capone, G. Noske, J. Umlauft, T. Beckers, A. Lederer, and S. Hirche. “Localized Active Learning of Gaussian Process State Space Models”.
In: Learning for Dynamics and Control. Ed. by Proceedings of Machine Learning Research. 2020. [Preprint] [bibtex]

T. Beckers, D. Kulić, and S. 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]

T. Beckers, S. Bansal, C. J. Tomlin, and S. 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]

T. Beckers, and S. 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]

T. Beckers, and S. 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]

T. Beckers, J. Umlauft, and S. 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]

J. Umlauft, T. Beckers, and S. 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]

T. Beckers, J. Umlauft, and S. 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]

T. Beckers, J. Umlauft, D. Kulić, and S. 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]

J. Umlauft, T. Beckers, M. Kimmel, and S. 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]

T. Beckers, and S. 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]

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

R. Geise, J. Schueuer, L. Thiele, K. Notte, T. Beckers, and A. 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

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

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

K. Friedl, W. Zhou, M. Rank, A. Ergin, T. Beckers, A. Mahdizadeh, A. Peer, S. Hirche. Haptik von Bedienelementen und Interieurkomponenten im Fahrzeug.
In: DFG Report SFB453 T5. 2014. [bibtex]