Thomas Seel
Photo: SCIoI
Thomas Seel’s research aims at synthesizing sensorimotor intelligence in robotic and biomedical systems by exploiting synergies between systems and control theory and machine learning. This includes the development of new methods for artificial motor learning and for information fusion in intelligent sensor networks as well as the application of these methods in soft robotic systems, autonomous vehicles, and biomimetic neuroprostheses. In 2023, he was appointed Head of the the Institute of Mechatronic Systems at Leibniz Universität Hannover. Earlier, he has held the professorship for Intelligent Sensorimotor Systems at the Department Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg, and he received a PhD degree in Systems and Control Theory from TU Berlin in 2016.
Projects
Thomas Seel is member of:
• Project 11: Dynamical Collective Adaptation and Learning
• Project 23: Control models of perceptual stability
• Project 23: Control models of perceptual stability
Publications +
2756394
Seel
1
apa
50
date
desc
year
20019
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Schweitzer, R., Doering, M., Seel, T., Raisch, J., & Rolfs, M. (2023). Saccadic omission revisited: What saccade-induced smear looks like [Preprint]. bioRxiv. https://doi.org/10.1101/2023.03.15.532538
Lehmann, D., Drebinger, P., Seel, T., & Raisch, J. (2023). Data-Driven Dynamic Input Transfer for Learning Control in Multi-Agent Systems with Heterogeneous Unknown Dynamics. 62nd IEEE Conference on Decision and Control (CDC). https://doi.org/10.1109/CDC49753.2023.10383433
Meindl, M., Molinari, F., Lehmann, D., & Seel, T. (2022). Collective Iterative Learning Control: Exploiting Diversity in Multi-Agent Systems for Reference Tracking Tasks. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/TCST.2021.3109646
Meindl, M., Lehmann, D., & Seel, T. (2022). Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics. Frontiers in Robotics and AI. https://doi.org/10.3389/frobt.2022.793512
Meindl, M., Molinari, F., Raisch, J., & Seel, T. (2020). Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation. IFAC-PapersOnLine 21st IFAC World Congress, 53(2), 1480–1486. https://doi.org/10.1016/j.ifacol.2020.12.1938
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