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arXiv · 2601.03247

Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data

Leonardo Bettini·Amirhossein Kazemipour·Robert K. Katzschmann·George Haller
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Abstract

Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Hydraulically amplified electrostatic (HASEL) actuators, a class of soft artificial muscles, offer high performance but exhibit memory effects and hysteresis. Here we present a data-driven reduction and control strategy grounded in spectral submanifold (SSM) theory. In the adiabatic regime, where inputs vary slowly relative to intrinsic transients, trajectories rapidly converge to a low-dimensional slow manifold. We learn an explicit input-to-output map on this manifold from forced-response trajectories alone, avoiding decay experiments that can trigger hysteresis. We deploy the SSM-based model for real-time control of an antagonistic HASEL-clutch joint. This approach yields a substantial reduction in tracking error compared to feedback-only and feedforward-only baselines under identical settings. This record-and-control workflow enables rapid characterization and high-performance control of soft muscles and muscle-driven joints without detailed physics-based modeling.

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Leonardo Bettini, Amirhossein Kazemipour, Robert K. Katzschmann, George Haller. 2026-01-06. Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data. https://arxiv.org/abs/2601.03247

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