Publications
You can also find my articles on my Google Scholar profile.
Preprints
A. Lederer, J. Umlauft, S. Hirche. “Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors”.
arXiv preprint arXiv:2307.04415, 2023.
R. Lefringhausen, S. Srithasan, A. Lederer, S. Hirche.“Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States”.
arXiv preprint arXiv:2307.04415, 2023.
Journal Articles
R. Römer1, A. Lederer1, S. Tesfazgi, S. Hirche. “Vision-Based Uncertainty-Aware Motion Planning based on Probabilistic Semantic Segmentation”.
IEEE Robotics and Automation Letters, 8(11): 7825 – 7832, 2023.
doi:10.1109/LRA.2023.3322899 [preprint] [video]
M. Omainska, J. Yamauchi, A. Lederer, S. Hirche, M. Fujita. “Rigid Motion Gaussian Processes with SE(3) Kernel and Application to Visual Pursuit Control”.
IEEE Control Systems Letters, 7: 2665 – 2670, 2023.
doi:10.1109/LCSYS.2023.3287507 [preprint]
A. Lederer, Z. Yang, J. Jiao, and S. Hirche. “Cooperative Control of Uncertain Multi-Agent Systems via Distributed Gaussian Processes”.
IEEE Transactions on Automatic Control, 68(5): 3091 – 3098, 2022.
doi:10.1109/TAC.2022.3205424 [preprint]
P. Bevanda, M. Beier, S. Kerz, A. Lederer, S. Sosnowski, S. Hirche. “Diffeomorphically Learning Stable Koopman Operators”.
IEEE Control Systems Letters, 6: 3427-3432, 2022.
doi:10.1109/LCSYS.2022.3184927 [preprint] [code]
A. Lederer, A. Capone, J. Umlauft, S. Hirche. “How Training Data Impacts Performance in Learning-based Control”.
IEEE Control Systems Letters, 5(3): 905 – 910, 2021.
doi:10.1109/LCSYS.2020.3006725 [preprint]
A. Capone, A. Lederer, J. Umlauft, S. Hirche. “Data Selection for Multi-Task Learning under Dynamic Constraints”.
IEEE Control Systems Letters, 5(3): 959 – 964, 2021.
doi:10.1109/LCSYS.2020.3006279 [preprint]
Conference Papers
P. Bevanda, M. Beier, A. Lederer, S. Sosnowski, E. Hüllermeier, S. Hirche. “Koopman Kernel Regression”.
Advances in Neural Information Processing Systems (Accepted), 2023. (acceptance rate: 26.1%)
[preprint] [code]
A. Lederer, E. Noorani, J. Baras, S. Hirche. “Risk-Sensitive Inhibitory Control for Safe Reinforcement Learning”.
Proceedings of the IEEE Conference on Decision and Control (Accepted), 2023.
[preprint]
A. Lederer1, A. Begzadic1, N. Das, S. Hirche. “Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions”.
IFAC-PapersOnLine 56 (2): 2250-2256, 2023.
doi:10.1016/j.ifacol.2023.10.1189 [preprint]
S. Tesfazgi1, A. Lederer1, J. F. Kunz, A. Ordóñez-Conejo, S. Hirche. “Model-Based Robot Control with Gaussian Process Online Learning: An Experimental Demonstration”.
IFAC-PapersOnLine 56(2): 501-506, 2023.
doi:10.1016/j.ifacol.2023.10.1617
N. Das, J. Umlauft, A. Lederer, A. Capone, T. Beckers, S. Hirche. “Deep Learning based Uncertainty Decomposition for Real-Time Control”.
IFAC-PapersOnLine 56(2): 847-853, 2023.
doi:10.1016/j.ifacol.2023.10.1671 [preprint]
X. Dai1, A. Lederer1, Z. Yang, S. Hirche. “Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning”.
Proceedings of the Conference on Learning for Dynamics and Control, pages 445 – 457, 2023.
paper:proceedings.mlr.press/v211/dai23a.html [preprint]
S. Curi1, A. Lederer1, S. Hirche, A. Krause. “Safe Reinforcement Learning via Confidence-Based Filters”.
Proceedings of the IEEE Conference on Decision and Control, pages 3409 – 3415, 2022.
doi:10.1109/CDC51059.2022.9992470 [preprint]
A. Lederer, M. Zhang, S. Tesfazgi, S. Hirche. “Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes”.
Proceedings of the IEEE Conference on Control Technology and Applications, pages 1285 – 1292, 2022.
doi:10.1109/CCTA49430.2022.9966043 [preprint] [code]
A. Ordonez-Conejo1, A. Lederer1, S. Hirche. “Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes”.
Proceedings of the European Control Conference, pages 2234 – 2240, 2022.
doi:10.23919/ECC55457.2022.9838285 [preprint]
A. Capone, A. Lederer, S. Hirche. “Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications”.
Proceedings of the International Conference on Machine Learning, pages 2609 – 2624, 2022. (acceptance rate: 21.9%)
paper:proceedings.mlr.press/v162/capone22a.html [preprint] [code]
Z. Yang, S. Sosnowski, Q. Liu, J Jiao, A. Lederer, S. Hirche. “Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems Based on Gaussian Processes”.
Proceedings of the IEEE Conference on Decision and Control, pages 4406 – 4411, 2021.
doi:10.1109/CDC45484.2021.9683522 [preprint]
S. Tesfazgi, A. Lederer, S. Hirche. “Inverse Reinforcement Learning: A Control Lyapunov Approach”.
Proceedings of the IEEE Conference on Decision and Control, pages 3627 – 3632, 2021.
doi:10.1109/CDC45484.2021.9683494 [preprint]
P. Budde gen. Dohmann1, A. Lederer1, M. Dißemond, S. Hirche. “Distributed Bayesian Online Learning for Cooperative Manipulation”.
Proceedings of the IEEE Conference on Decision and Control, pages 2888 – 2895, 2021.
doi:10.1109/CDC45484.2021.9683772 [preprint]
A. Lederer, A. Ordonez-Conejo, K. Maier, W. Xiao, J. Umlauft, S. Hirche. “Gaussian Process-Based Real-Time Learning for Safety Critical Applications”.
Proceedings of the International Conference on Machine Learning, pages 6055 – 6064, 2021. (acceptance rate: 21.5%)
paper:proceedings.mlr.press/v139/lederer21a.html [preprint] [code]
A. Lederer, A. Capone, T. Beckers, J. Umlauft, S. Hirche. “The Impact of Data on the Stability of Learning-Based Control”.
Proceedings of the Conference on Learning for Dynamics and Control, pages 623 – 635, 2021.
paper:proceedings.mlr.press/v144/lederer21a.html
A. Lederer, Q. Hao, S. Hirche. “Confidence Regions for Simulations with Learned Probabilistic Models”.
Proceedings of the American Control Conference, pages 3947 – 3952, 2020.
doi:10.23919/ACC45564.2020.9147978 [preprint] [code]
A. Capone, A. Lederer, S. Hirche. “Confidence Regions for Predictions of Online Learning-Based Control”.
Proceedings of the 21th IFAC World Congress, volume 53 of IFAC-PapersOnLine, pages 983 – 988, 2020.
doi:10.1016/j.ifacol.2020.12.1278
A. Lederer, M. Kessler, S. Hirche. “GP3: A Sampling-based Analysis Framework for Gaussian Processes”.
Proceedings of the 21th IFAC World Congress, volume 53 of IFAC-PapersOnLine, pages 983 – 988, 2020.
doi:10.1016/j.ifacol.2020.12.1273 [preprint] [code]
W. Xiao1, A. Lederer1, S. Hirche. “Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression”.
Proceedings of the 21th IFAC World Congress, volume 53 of IFAC-PapersOnLine, pages 1194 – 1199, 2020.
doi:10.1016/j.ifacol.2020.12.1335 [preprint]
A. Capone, G. Noske, J. Umlauft, T. Beckers, A. Lederer, S. Hirche. “Localized Active Learning of Gaussian Process State Space Models”.
Proceedings of the Conference on Learning for Dynamics and Control, pages 490 – 499, 2020.
paper:proceedings.mlr.press/v120/capone20a.html [preprint]
A. Lederer, A. Capone, S. Hirche. “Parameter Optimization for Learning-based Control of Control-Affine Systems”.
Proceedings of the Conference on Learning for Dynamics and Control, pages 465 – 475, 2020.
paper:proceedings.mlr.press/v120/lederer20a.html [preprint] [code]
J. Umlauft, T. Beckers, A. Capone, A. Lederer, S. Hirche. “Smart Forgetting for Safe Online Learning with Gaussian Processes”.
Proceedings of the Conference on Learning for Dynamics and Control, pages 160 – 169, 2020.
paper:proceedings.mlr.press/v120/umlauft20a.html [preprint]
A. Lederer, S. Hirche. “Local Asymptotic Stability Analysis and Region of Attraction Estimation with Gaussian Processes”.
Proceedings of the IEEE Conference on Decision and Control, pages 1766 – 1771, 2019.
doi:10.1109/CDC40024.2019.9029489 [preprint]
A. Lederer, J. Umlauft, S. Hirche. “Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control”.
Advances in Neural Information Processing Systems, pages 659 – 669, 2019. (acceptance rate: 21.6%)
paper:proceedings.neurips.cc/paper/2019 [code]
J. Umlauft, A. Lederer, S. Hirche. “Learning Stable Gaussian Process State Space Models”.
Proceedings of the American Control Conference, pages 1499 – 1504, 2017.
doi:10.23919/ACC.2017.7963165 [preprint]
Miscellaneous
A. Lederer. “Gaussian Processes in Control: Performance Guarantees through Efficient Learning”
PhD Thesis, Technical University of Munich, 2023.
link
A. Lederer, J. Umlauft, S. Hirche. “Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control”.
arXiv preprint arXiv:2101.05328, 2021.
A. Lederer, J. Umlauft, S. Hirche. “Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves”.
arXiv preprint arXiv:1906.01404, 2019.