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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Vydáno v:	IEEE transactions on robotics s. 1 - 18
Hlavní autoři:	Habich, Tim-Lukas, Mohammad, Aran, Ehlers, Simon F. G., Bensch, Martin, Seel, Thomas, Schappler, Moritz
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	IEEE 2025
Témata:	and learning for soft robots control model learning for control Modeling optimization and optimal control physics-informed machine learning
ISSN:	1552-3098, 1941-0468
On-line přístup:	Získat plný text
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