Publications

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

Journals

• 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 (accepted), 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 (accepted), 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

• Abdullah Tokmak, Thomas B. Schön, and Dominik Baumann, “PACSBO: Probably approximately correct safe Bayesian optimization,” Symposium on Systems Theory in Data and Optimization, Stuttgart, Germany, 2024, arXiv.
• Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, and Paweł Wachel, “A computationally lightweight safe learning algorithm,” IEEE Conference on Decision and Control, Singapore, Singapore, 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, Yokohama, Japan, 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, Philadelphia, PA, USA, 2023, arXiv.
• Alexander Gräfe, Dominik Baumann, and Sebastian Trimpe, “Towards remote fault detection by analyzing communication priorities,” IEEE Conference on Decision and Control, Cancun, Mexico, 2022, arXiv.
• Dominik Baumann, Alonso Marco, Matteo Turchetta, and Sebastian Trimpe, “GoSafe: Globally optimal safe robot learning,” IEEE International Conference on Robotics and Automation, Xi’an, China, 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, Chicago, IL, USA, 2019, arXiv.
• Dominik Baumann, Friedrich Solowjow, Karl H. Johansson, and Sebastian Trimpe, “Event-triggered pulse control with model learning (if necessary),” American Control Conference, Philadelphia, PA, USA, 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), Montréal, Canada, 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, Miami, FL, USA, 2018, arXiv.
• Friedrich Solowjow, Dominik Baumann, Jochen Garcke, and Sebastian Trimpe, “Event-triggered learning for resource-efficient networked control,” American Control Conference, Milwaukee, WI, USA, 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, Porto, Portugal, 2018, arXiv.

Reports

• 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.