Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks

Soft robots can revolutionize several applications with high demands on dexterity and safety. When operating these systems, real-time estimation and control require fast and accurate models. However, prediction with first-principles (FP) models is slow, and learned black-box models have poor general...

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Veröffentlicht in:	IEEE transactions on robotics S. 1 - 18
Hauptverfasser:	Habich, Tim-Lukas, Mohammad, Aran, Ehlers, Simon F. G., Bensch, Martin, Seel, Thomas, Schappler, Moritz
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 2025
Schlagworte:	and learning for soft robots control model learning for control Modeling optimization and optimal control physics-informed machine learning
ISSN:	1552-3098, 1941-0468
Online-Zugang:	Volltext
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