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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.11567 |
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| _version_ | 1866914036889681920 |
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| author | Ristich, Eron Wang, Jiahe Zhang, Lei Ali, Sultan Haidar Jin, Wanxin Ren, Yi Sun, Jiefeng |
| author_facet | Ristich, Eron Wang, Jiahe Zhang, Lei Ali, Sultan Haidar Jin, Wanxin Ren, Yi Sun, Jiefeng |
| contents | Soft continuum robots can allow for biocompatible yet compliant motions, such as the ability of octopus arms to swim, crawl, and manipulate objects. However, current state-of-the-art continuum robots can only achieve real-time task-space control (i.e., tip control) but not whole-shape control, mainly due to the high computational cost from its infinite degrees of freedom. In this paper, we present a data-driven Koopman operator-based approach for the shape control of simulated multi-segment tendon-driven soft continuum robots with the Kirchhoff rod model. Using data collected from these simulated soft robots, we conduct a per-segment projection scheme on the state of the robots allowing for the identification of control-affine Koopman models that are an order of magnitude more accurate than without the projection scheme. Using these learned Koopman models, we use a linear model predictive control (MPC) to control the robots to a collection of target shapes of varying complexity. Our method realizes computationally efficient closed-loop control, and demonstrates the feasibility of real-time shape control for soft robots. We envision this work can pave the way for practical shape control of soft continuum robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Shape control of simulated multi-segment continuum robots via Koopman operators with per-segment projection Ristich, Eron Wang, Jiahe Zhang, Lei Ali, Sultan Haidar Jin, Wanxin Ren, Yi Sun, Jiefeng Robotics Soft continuum robots can allow for biocompatible yet compliant motions, such as the ability of octopus arms to swim, crawl, and manipulate objects. However, current state-of-the-art continuum robots can only achieve real-time task-space control (i.e., tip control) but not whole-shape control, mainly due to the high computational cost from its infinite degrees of freedom. In this paper, we present a data-driven Koopman operator-based approach for the shape control of simulated multi-segment tendon-driven soft continuum robots with the Kirchhoff rod model. Using data collected from these simulated soft robots, we conduct a per-segment projection scheme on the state of the robots allowing for the identification of control-affine Koopman models that are an order of magnitude more accurate than without the projection scheme. Using these learned Koopman models, we use a linear model predictive control (MPC) to control the robots to a collection of target shapes of varying complexity. Our method realizes computationally efficient closed-loop control, and demonstrates the feasibility of real-time shape control for soft robots. We envision this work can pave the way for practical shape control of soft continuum robots. |
| title | Shape control of simulated multi-segment continuum robots via Koopman operators with per-segment projection |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.11567 |