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

• URL: http://arxiv.org/abs/2502.01916v1
• Date: Tue, 04 Feb 2025 01:16:33 GMT
• Title: Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks
• Authors: Tim-Lukas Habich, Aran Mohammad, Simon F. G. Ehlers, Martin Bensch, Thomas Seel, Moritz Schappler,
• Abstract summary: We propose physics-informed neural networks (PINNs) for articulated soft robots (ASRs) with a focus on data efficiency.<n>The amount of expensive real-world training data is reduced to a minimum - one dataset in one system domain.<n>The prediction speed of an accurate FP model is improved with the PINN by up to a factor of 466 at slightly reduced accuracy.
• Score: 4.146337610044239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 generalizability. Physics-informed machine learning offers excellent advantages here, but it is currently limited to simple, often simulated systems without considering changes after training. We propose physics-informed neural networks (PINNs) for articulated soft robots (ASRs) with a focus on data efficiency. The amount of expensive real-world training data is reduced to a minimum - one dataset in one system domain. Two hours of data in different domains are used for a comparison against two gold-standard approaches: In contrast to a recurrent neural network, the PINN provides a high generalizability. The prediction speed of an accurate FP model is improved with the PINN by up to a factor of 466 at slightly reduced accuracy. This enables nonlinear model predictive control (MPC) of the pneumatic ASR. In nine dynamic MPC experiments, an average joint-tracking error of 1.3{\deg} is achieved.

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