Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908868549804032 |
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| author | Hao, Wenjian Fang, Yuxuan Lu, Zehui Mou, Shaoshuai |
| author_facet | Hao, Wenjian Fang, Yuxuan Lu, Zehui Mou, Shaoshuai |
| contents | This paper presents an efficient model predictive path integral (MPPI) control framework for systems with complex nonlinear dynamics. To improve the computational efficiency of classic MPPI while preserving control performance, we replace the nonlinear dynamics used for trajectory propagation with a learned linear deep Koopman operator (DKO) model, enabling faster rollout and more efficient trajectory sampling. The DKO dynamics are learned directly from interaction data, eliminating the need for analytical system models. The resulting controller, termed MPPI-DK, is evaluated in simulation on pendulum balancing and surface vehicle navigation tasks, and validated on hardware through reference-tracking experiments on a quadruped robot. Experimental results demonstrate that MPPI-DK achieves control performance close to MPPI with true dynamics while substantially reducing computational cost, enabling efficient real-time control on robotic platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05385 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics Hao, Wenjian Fang, Yuxuan Lu, Zehui Mou, Shaoshuai Robotics Systems and Control This paper presents an efficient model predictive path integral (MPPI) control framework for systems with complex nonlinear dynamics. To improve the computational efficiency of classic MPPI while preserving control performance, we replace the nonlinear dynamics used for trajectory propagation with a learned linear deep Koopman operator (DKO) model, enabling faster rollout and more efficient trajectory sampling. The DKO dynamics are learned directly from interaction data, eliminating the need for analytical system models. The resulting controller, termed MPPI-DK, is evaluated in simulation on pendulum balancing and surface vehicle navigation tasks, and validated on hardware through reference-tracking experiments on a quadruped robot. Experimental results demonstrate that MPPI-DK achieves control performance close to MPPI with true dynamics while substantially reducing computational cost, enabling efficient real-time control on robotic platforms. |
| title | Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2603.05385 |