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arXiv · 2609.14376

Orientation Control of Soft Robots via Adiabatic Spectral Submanifolds

Aron Karakai·Roshan S. Kaundinya·Mike Yan Michelis·Robert Katzschmann·George Haller
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Abstract

Soft robots are commonly sought for safety-critical interactions in delicate environments, where accurate position and orientation control is imperative. Model predictive control (MPC) offers a solution, but it requires a model of the robot's infinite-dimensional nonlinear dynamics that is at once accurate and computationally cheap. Recent theory on adiabatic spectral submanifolds (aSSMs) and their applications to soft robots provide data-driven model-reduction methods to construct such models. Here, we extend these methods to identify aSSMs from enlarged observable datasets and upgrade the currently available aSSM-MPC schemes. Evaluated on a high-fidelity finite-element simulation of a pressure-actuated soft arm, our controller reduces position and orientation tracking error by more than 60% compared to existing data-driven baselines.

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Aron Karakai, Roshan S. Kaundinya, Mike Yan Michelis, Robert Katzschmann, George Haller. 2026-09-13. Orientation Control of Soft Robots via Adiabatic Spectral Submanifolds. https://arxiv.org/abs/2609.14376

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