Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910058695098368 |
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| author | Jung, Sebin Abuduweili, Abulikemu Li, Jiaxing Liu, Changliu |
| author_facet | Jung, Sebin Abuduweili, Abulikemu Li, Jiaxing Liu, Changliu |
| contents | Controlling robots with strongly nonlinear, high-dimensional dynamics remains challenging, as direct nonlinear optimization with safety constraints is often intractable in real time. The Koopman operator offers a way to represent nonlinear systems linearly in a lifted space, enabling the use of efficient linear control. We propose a data-driven framework that learns a Koopman embedding and operator from data, and integrates the resulting linear model with the Safe Set Algorithm (SSA). This allows the tracking and safety constraints to be solved in a single quadratic program (QP), ensuring feasibility and optimality without a separate safety filter. We validate the method on a Kinova Gen3 manipulator and a Go2 quadruped, showing accurate tracking and obstacle avoidance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03740 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics Jung, Sebin Abuduweili, Abulikemu Li, Jiaxing Liu, Changliu Robotics Controlling robots with strongly nonlinear, high-dimensional dynamics remains challenging, as direct nonlinear optimization with safety constraints is often intractable in real time. The Koopman operator offers a way to represent nonlinear systems linearly in a lifted space, enabling the use of efficient linear control. We propose a data-driven framework that learns a Koopman embedding and operator from data, and integrates the resulting linear model with the Safe Set Algorithm (SSA). This allows the tracking and safety constraints to be solved in a single quadratic program (QP), ensuring feasibility and optimality without a separate safety filter. We validate the method on a Kinova Gen3 manipulator and a Go2 quadruped, showing accurate tracking and obstacle avoidance. |
| title | Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.03740 |