Publications

You can also find my articles on my Google Scholar profile.

Journals

• Dominik Baumann, Erfaun Noorani, Arsenii Mustafin, Xinyi Sheng, Bert Verbruggen, Arne Vanhoyweghen, Vincent Ginis, and Thomas B. Schön, “Ergodicity in reinforcement learning,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2026, arXiv.
• Abdullah Tokmak, Thomas B. Schön, and Dominik Baumann, “Safe Bayesian optimization across noise models via scenario programming,” IEEE Control Systems Letters, 2025, arXiv.
• Dominik Baumann, Krzysztof Kowalczyk, Cristian R. Rojas, Koen Tiels, and Paweł Wachel, “Safety and optimality in learning-based control at low computational cost,” IEEE Transactions on Automatic Control, 2025, arXiv.
• Shiming He, Alexander von Rohr, Dominik Baumann, Ji Xiang, and Sebastian Trimpe, “Simulation-aided policy tuning for black-box robot learning,” IEEE Transactions on Robotics, 2025, arXiv.
• Dominik Baumann, Erfaun Noorani, James Price, Ole Peters, Colm Connaughton, and Thomas B. Schön, “Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations,” Transactions on Machine Learning Research, 2025, arXiv.
• Dominik Baumann and Thomas B. Schön, “Safe reinforcement learning in uncertain contexts,” IEEE Transactions on Robotics, 2024, arXiv.
• Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, and Dominik Baumann, “GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems,” Artificial Intelligence, 2023, arXiv.
• Dominik Baumann, Friedrich Solowjow, Karl H. Johansson, and Sebastian Trimpe, “Identifying causal structure in dynamical systems,” Transactions on Machine Learning Research, 2022, arXiv.
• Fabian Mager, Dominik Baumann, Carsten Herrmann, Sebastian Trimpe, and Marco Zimmerling, “Scaling beyond bandwidth limitations: Wireless control with stability guarantees under overload,” ACM Transactions on Cyber-Physical Systems, 2022, arXiv.
• Dominik Baumann, Fabian Mager, Ulf Wetzker, Lothar Thiele, Marco Zimmerling, and Sebastian Trimpe, “Wireless control for smart manufacturing: Recent approaches and open challenges,” Proceedings of the IEEE, 2021, arXiv.
• Niklas Funk, Dominik Baumann, Vincent Berenz, and Sebastian Trimpe, “Learning event-triggered control from data through joint optimization,” IFAC Journal of Systems and Control, 2021, arXiv.
• Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, and Sebastian Trimpe, “Robot learning with crash constraints,” IEEE Robotics and Automation Letters, 2021, arXiv.
• Dominik Baumann, Fabian Mager, Marco Zimmerling, and Sebastian Trimpe, “Control-guided communication: Efficient resource arbitration and allocation in multi-hop wireless control systems,” IEEE Control Systems Letters, 2020, arXiv.
• Dominik Baumann, Fabian Mager, Romain Jacob, Lothar Thiele, Marco Zimmerling, and Sebastian Trimpe, “Fast feedback control over multi-hop wireless networks with mode changes and stability guarantees,” ACM Transactions on Cyber-Physical Systems, 2019, arXiv.
• Sebastian Trimpe and Dominik Baumann, “Resource-aware IoT control: Saving communication through predictive triggering,” IEEE Internet of Things Journal, 2019, arXiv.
• Alon Ascoli, Dominik Baumann, Ronald Tetzlaff, Leon O. Chua, and Manfred Hild, “Memristor-enhanced humanoid robot control system–Part I: Theory behind the novel memcomputing paradigm,” International Journal of Circuit Theory and Applications, 2018.
• Dominik Baumann, Alon Ascoli, Ronald Tetzlaff, Leon O. Chua, and Manfred Hild, “Memristor-enhanced humanoid robot control system–Part II: Circuit theoretic model and performance analysis,” International Journal of Circuit Theory and Applications, 2018.

Conferences

• Shreya Das, Kundan Kumar, Muhammad Iqbal, Outi Savolainen, Dominik Baumann, Laura Ruotsalainen, and Simo Särkkä, “Integrating Lagrangian neural networks into the Dyna framework for reinforcement learning,” European Signal Processing Conference, 2026, arXiv.
• Sara Pérez-Vieites, Sahel Iqbal, Simo Särkkä, and Dominik Baumann, “Online Bayesian experimental design for partially observed dynamical systems,” International Conference on Machine Learning, 2026, arXiv.
• Qingyun Guo, Junyi Shi, Tomasz P. Kucner, and Dominik Baumann, “Priority-driven control and communication in decentralized multi-agent systems via reinforcement learning,” IFAC World Congress, 2026 (accepted).
• Amirsaman Mirjalili, Elham Kowsari, Dominik Baumann, and Reza Ghabcheloo, “IMU to joint extrinsic calibration of articulated link pairs for heavy-duty machinery,” IFAC World Congress, 2026 (accepted).
• Shreeram Murali, Cristian R. Rojas, and Dominik Baumann, “Computationally lightweight classifiers with frequentist bounds on predictions,” International Conference on Artificial Intelligence and Statistics, 2026, arXiv.
• Abdullah Tokmak, Thomas B. Schön, and Dominik Baumann, “Towards safe control parameter tuning in distributed multi-agent systems,” IEEE Conference on Decision and Control, 2025, arXiv.
• Xinyi Sheng and Dominik Baumann, “Beyond expected value: geometric mean optimization for long-term policy performance in reinforcement learning,” IEEE Conference on Decision and Control, 2025, arXiv.
• Maryam K. Eskeri, Ville Kyrki, Dominik Baumann, and Tomasz P. Kucner, “Efficient human-aware task allocation for multi-robot systems in shared environments,” IEEE/RSJ International Conference on Intelligent Robots and Systems, 2025, arXiv.
• Maryam K. Eskeri, Thomas Wiedemann, Ville Kyrki, Dominik Baumann, and Tomasz P. Kucner, “A lightweight crowd model for robot social navigation,” European Conference on Mobile Robots, 2025, arXiv.
• Mingwei Deng, Ville Kyrki, and Dominik Baumann, “Transfer learning in latent contextual bandits with covariate shift through causal transportability,” Conference on Causal Learning and Reasoning, 2025, arXiv.
• Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, and Dominik Baumann, “Safe exploration in reproducing kernel Hilbert spaces,” International Conference on Artificial Intelligence and Statistics, 2025, arXiv.
• Abdullah Tokmak, Thomas B. Schön, and Dominik Baumann, “PACSBO: Probably approximately correct safe Bayesian optimization,” Symposium on Systems Theory in Data and Optimization, 2024, arXiv.
• Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, and Paweł Wachel, “A computationally lightweight safe learning algorithm,” IEEE Conference on Decision and Control, 2023, arXiv.
• Dominik Baumann and Thomas B. Schön, “On the trade-off between event-based and periodic state estimation under bandwidth constraints,” IFAC World Congress, 2023, arXiv.
• Lukas Kesper, Sebastian Trimpe, and Dominik Baumann, “Toward multi-agent reinforcement learning for distributed event-triggered control,” Learning for Dynamics and Control Conference, 2023, arXiv.
• Alexander Gräfe, Dominik Baumann, and Sebastian Trimpe, “Towards remote fault detection by analyzing communication priorities,” IEEE Conference on Decision and Control, 2022, arXiv.
• Dominik Baumann, Alonso Marco, Matteo Turchetta, and Sebastian Trimpe, “GoSafe: Globally optimal safe robot learning,” IEEE International Conference on Robotics and Automation, 2021, arXiv.
• José M. Mastrangelo, Dominik Baumann, and Sebastian Trimpe, “Predictive triggering for distributed control of resource constrained multi-agent systems,” IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2019, arXiv.
• Dominik Baumann, Friedrich Solowjow, Karl H. Johansson, and Sebastian Trimpe, “Event-triggered pulse control with model learning (if necessary),” American Control Conference, 2019, arXiv.
• Fabian Mager, Dominik Baumann, Romain Jacob, Lothar Thiele, Sebastian Trimpe, and Marco Zimmerling, “Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks,” ACM/IEEE International Conference on Cyber-Physical Systems, (Best Paper Award), 2019, arXiv.
• Dominik Baumann, Jia-Jie Zhu, Gerog Martius, and Sebastian Trimpe, “Deep reinforcement learning for event-triggered control,” IEEE Conference on Decision and Control, 2018, arXiv.
• Friedrich Solowjow, Dominik Baumann, Jochen Garcke, and Sebastian Trimpe, “Event-triggered learning for resource-efficient networked control,” American Control Conference, 2018, arXiv.
• Dominik Baumann, Fabian Mager, Harsoveet Singh, Marco Zimmerling, and Sebastian Trimpe, “Evaluating low-power wireless cyber-physical systems,” IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems, 2018, arXiv.

Reports

• Dominik Baumann, Konrad Buck, Sabine Demsar, Michael Dietrich, David Fischer, Patrick Glauner, Milena Rapp, Larissa Rohr, Svenja Rösch, Michael von Uechtritz und Steinkirch und Christopher Nehring, “Key2KI: Verantwortungsvoller Einsatz von Künstlicher Intelligenz in Politik und Wahlkampf”, Baden-Württemberg Stiftung, 2025, PDF.
• Dominik Baumann, “An ergodicity perspective on reinforcement learning,” Ergodicity Economics Blog, 2023.
• Oliver Hulme, Arne Vanhoyweghen, Colm Connaughton, Ole Peters, Simon Steinkamp, Alexander Adamou, Dominik Baumann, Vincent Ginis, Bert Verbruggen, James Price, and Benjamin Skjold, “Reply to ‘The limitations of growth-optimal approaches to decision making under uncertainty,’” Econ Journal Watch, 2023, PDF.
• Sebastian Weichwald, Søren W. Mogensen, Tabitha E. Lee, Dominik Baumann, Oliver Kroemer, Isabelle Guyon, Sebastian Trimpe, Jonas Peters, and Niklas Pfister, “Learning by doing: Controlling a dynamical system using causality, control, and reinforcement learning,” NeurIPS Competition and Demonstration Track, 2021, arXiv.
• Peter M. Addo, Dominik Baumann, Juliet McMurren, Stefaan G. Verhulst, Andrew Young, and Andrew J. Zahuranec, “Emerging uses of technology for development: A new intelligence paradigm,” Policy Paper, Agence Française de Développement, 2020, SRRN.

Theses

• Dominik Baumann, “Learning and control strategies for cyber-physcial systems: From wireless control over deep reinforcement learning to causal identification,” Ph.D. Thesis, KTH Stockholm, Sweden, 2020, DiVA.
• Dominik Baumann, “Fast and resource-efficient control of wireless cyber-physical systems,” Licentiate Thesis, KTH Stockholm, Sweden, 2019, DiVA.
• Dominik Baumann, “Numerical study and analysis of a novel cognitive sensorimotor loop based on circuit elements with memory,” Diploma Thesis, TU Dresden, Germany, 2016.