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arXiv · 2305.05375

Physics-informed Neural Networks to Model and Control Robots: a Theoretical and Experimental Investigation

Jingyue Liu·Pablo Borja·Cosimo Della Santina
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Abstract

This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems. Achieving this goal required extending Physics Informed Neural Networks to handle non-conservative effects. We propose to combine these learned models with model-based controllers originally developed with first-principle models in mind. By combining standard and new techniques, we can achieve precise control performance while proving theoretical stability bounds. These validations include real-world experiments of motion prediction with a soft robot and of trajectory tracking with a Franka Emika manipulator.

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Jingyue Liu, Pablo Borja, Cosimo Della Santina. 2023-05-09. Physics-informed Neural Networks to Model and Control Robots: a Theoretical and Experimental Investigation. https://arxiv.org/abs/2305.05375

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