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

TL;DR

This paper introduces a data-driven spectral submanifold approach for modeling and controlling soft artificial muscles, enabling real-time control and reduced tracking error without detailed physics models.

Contribution

It presents a novel spectral submanifold-based method for data-driven modeling and control of nonlinear soft actuators, avoiding complex physics-based modeling.

Findings

Significant reduction in tracking error using the SSM-based control

Effective real-time control of HASEL actuators demonstrated

Model captures nonlinear dynamics without decay experiments

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.