Data-Driven Soft Robot Control via Adiabatic Spectral Submanifolds
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918171704819712 |
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| author | Kaundinya, Roshan S. Alora, John Irvin Matt, Jonas G. Pabon, Luis A. Pavone, Marco Haller, George |
| author_facet | Kaundinya, Roshan S. Alora, John Irvin Matt, Jonas G. Pabon, Luis A. Pavone, Marco Haller, George |
| contents | 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 our data-driven model's effectiveness in tracking dynamic trajectories across diverse tasks, validated on a high-fidelity, high-dimensional finite-element model of a soft trunk robot and a Cosserat rod-based elastic soft arm. 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_10919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Soft Robot Control via Adiabatic Spectral Submanifolds Kaundinya, Roshan S. Alora, John Irvin Matt, Jonas G. Pabon, Luis A. Pavone, Marco Haller, George Robotics Systems and Control Pattern Formation and Solitons 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 our data-driven model's effectiveness in tracking dynamic trajectories across diverse tasks, validated on a high-fidelity, high-dimensional finite-element model of a soft trunk robot and a Cosserat rod-based elastic soft arm. 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. |
| title | Data-Driven Soft Robot Control via Adiabatic Spectral Submanifolds |
| topic | Robotics Systems and Control Pattern Formation and Solitons |
| url | https://arxiv.org/abs/2503.10919 |