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This research advances soft robot control by leveraging adiabatic spectral submanifolds to create highly efficient data-driven models that capture complex nonlinear dynamics. By reducing high-dimensional finite-element systems into low-dimensional manifolds, the approach achieves tracking performance up to ten times better than existing linear methods while remaining robust against experimental noise. The technique offers a significant leap forward for deploying intelligent control strategies on physically compliant robots without relying on computationally expensive full-order simulations.
Data-Driven Soft Robot Control via Adiabatic Spectral Submanifolds
arXiv:2503.10919v3 Announce Type: replace Abstract: The mechanical complexity of soft robots creates significant challenges for their model-based control. Specifically, linear data-driven models have struggled to control soft robots on complex, spatially extended paths that explore regions with significant nonlinear behavior. To account for these nonlinearities, we develop here a model-predictive control strategy based on the recent theory of adiabatic spectral submanifolds (aSSMs). This theory is applicable because the internal vibrations of heavily overdamped robots decay at a speed that is much faster than the desired speed of the robot along its intended path. In that case, low-dimensional attracting invariant manifolds (aSSMs) emanate from the path and carry the dominant dynamics of the robot. Aided by this recent theory, we devise an aSSM-based model-predictive control scheme purely from data. We demonstrate the effectiveness of our data-driven model in tracking dynamic trajectories across diverse tasks. We validate on high-fidelity, high-dimensional finite-element models of a soft trunk robot and Cosserat-rod-based elastic soft arms, with additional experiments confirming robust performance even in the presence of experimental noise. Notably, we find that five- or six-dimensional aSSM-reduced models outperform the tracking performance of other data-driven modeling methods by a factor up to 10 across all closed-loop control tasks.
Abstract:The mechanical complexity of soft robots creates significant challenges for their model-based control. Specifically, linear data-driven models have struggled to control soft robots on complex, spatially extended paths that explore regions with significant nonlinear behavior. To account for these nonlinearities, we develop here a model-predictive control strategy based on the recent theory of adiabatic spectral submanifolds (aSSMs). This theory is applicable because the internal vibrations of heavily overdamped robots decay at a speed that is much faster than the desired speed of the robot along its intended path. In that case, low-dimensional attracting invariant manifolds (aSSMs) emanate from the path and carry the dominant dynamics of the robot. Aided by this recent theory, we devise an aSSM-based model-predictive control scheme purely from data. We demonstrate the effectiveness of our data-driven model in tracking dynamic trajectories across diverse tasks. We validate on high-fidelity, high-dimensional finite-element models of a soft trunk robot and Cosserat-rod-based elastic soft arms, with additional experiments confirming robust performance even in the presence of experimental noise. Notably, we find that five- or six-dimensional aSSM-reduced models outperform the tracking performance of other data-driven modeling methods by a factor up to 10 across all closed-loop control tasks.
Robotics (cs.RO); Systems and Control (eess.SY); Pattern Formation and Solitons (nlin.PS)
arXiv:2503.10919 [cs.RO]
(or arXiv:2503.10919v3 [cs.RO] for this version)
From: Roshan S. Kaundinya [view email] [v1] Thu, 13 Mar 2025 22:14:48 UTC (29,933 KB) [v2] Mon, 27 Oct 2025 17:16:54 UTC (26,059 KB) [v3] Thu, 11 Jun 2026 15:28:00 UTC (27,178 KB)
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