Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915886986690560 |
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| author | Wolff, Paul Leonard Buurmeijer, Hugo Pabon, Luis Alora, John Irvin Leone, Mark Kaundinya, Roshan S. Kazemipour, Amirhossein Katzschmann, Robert K. Pavone, Marco |
| author_facet | Wolff, Paul Leonard Buurmeijer, Hugo Pabon, Luis Alora, John Irvin Leone, Mark Kaundinya, Roshan S. Kazemipour, Amirhossein Katzschmann, Robert K. Pavone, Marco |
| contents | Continuum robots exhibit high-dimensional, nonlinear dynamics which are often coupled with their actuation mechanism. Spectral submanifold (SSM) reduction has emerged as a leading method for reducing high-dimensional nonlinear dynamical systems to low-dimensional invariant manifolds. Our proposed control-augmented SSMs (caSSMs) extend this methodology by explicitly incorporating control inputs into the state representation, enabling these models to capture nonlinear state-input couplings. Training these models relies solely on controlled decay trajectories of the actuator-augmented state, thereby removing the additional actuation-calibration step commonly needed by prior SSM-for-control methods. We learn a compact caSSM model for a tendon-driven trunk robot, enabling real-time control and reducing open-loop prediction error by 40% compared to existing methods. In closed-loop experiments with model predictive control (MPC), caSSM reduces tracking error by 52%, demonstrating improved performance against Koopman and SSM based MPC and practical deployability on hardware continuum robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23044 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots Wolff, Paul Leonard Buurmeijer, Hugo Pabon, Luis Alora, John Irvin Leone, Mark Kaundinya, Roshan S. Kazemipour, Amirhossein Katzschmann, Robert K. Pavone, Marco Robotics Continuum robots exhibit high-dimensional, nonlinear dynamics which are often coupled with their actuation mechanism. Spectral submanifold (SSM) reduction has emerged as a leading method for reducing high-dimensional nonlinear dynamical systems to low-dimensional invariant manifolds. Our proposed control-augmented SSMs (caSSMs) extend this methodology by explicitly incorporating control inputs into the state representation, enabling these models to capture nonlinear state-input couplings. Training these models relies solely on controlled decay trajectories of the actuator-augmented state, thereby removing the additional actuation-calibration step commonly needed by prior SSM-for-control methods. We learn a compact caSSM model for a tendon-driven trunk robot, enabling real-time control and reducing open-loop prediction error by 40% compared to existing methods. In closed-loop experiments with model predictive control (MPC), caSSM reduces tracking error by 52%, demonstrating improved performance against Koopman and SSM based MPC and practical deployability on hardware continuum robots. |
| title | Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.23044 |