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

• URL: http://arxiv.org/abs/2601.03247v1
• Date: Tue, 06 Jan 2026 18:43:49 GMT
• Title: Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data
• Abstract summary: Hydraulically electrostatic (HASEL) actuators offer high performance but exhibit memory effects and musculoskeletal effects.<n>We present a data-driven reduction and control strategy grounded in spectral submanifold (SSM) theory.<n>We deploy the SSM-based model for real-time control of an antagonistic HASEL-clutch joint.
• Score: 6.770726279203919
• License: http://creativecommons.org/licenses/by/4.0/
• 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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