Robotic Systems

Domain-Decoupled Physics-informed Neural Networks with Closed-Form Gradients for Fast Model Learning of Dynamical Systems
Krauss, H., Habich, T.-L., Bartholdt, M., Seel, T. & Schappler, M., 2024, Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics. Band 1. S. 55-66 12 S. (Proceedings of the International Conference on Informatics in Control, Automation and Robotics).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Open Access
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline
Ewering, J.-H., Volkmann, B., Ehlers, S. F. G., Seel, T. & Meindl, M. B., 16 Dez. 2024, 2024 IEEE 63rd Conference on Decision and Control, CDC 2024. Institute of Electrical and Electronics Engineers Inc., S. 4157-4164 8 S. (Proceedings of the IEEE Conference on Decision and Control).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
Accurate and agile control of a pneumatic robotic actuator by GP-based feedforward learning
Pawluchin, A., Meindl, M., Seel, T. & Boblan, I., 29 März 2023, in: Proceedings on Automation in Medical Engineering. 2, 1, 753.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review

Open Access

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