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About this paper
Chengsi Yao, Ge Wang, Kai Kang, Shenhao Yan, Jiahao Yang, Fan Feng, Honghao Cai, Xianxian Zeng and 4 more
KoopmanFlow introduces a generative control policy for robotics that employs a Koopman-inspired structural bias to spectrally decouple stable macroscopic motions from visually-driven high-frequency reactive corrections. This methodology enables high-fidelity, one-step control at 30 FPS, demonstrating improved performance in real-time robotic manipulation across contact-rich and long-horizon tasks.
Key insights
- Decomposing action dynamics into distinct spectral components (time-invariant inertia and time-variant residuals) effectively mitigates "spectral confounding," enabling more accurate modeling of stable global motions and agile local corrections.
- A Koopman-inspired structural prior, when integrated with consistency distillation and Flow Matching, facilitates high-fidelity, single-step generative control by efficiently collapsing iterative denoising into localized linear algebra operations.
- High-frequency robotic reactive corrections are often visually-driven, and explicitly associating these transients with semantic visual events via a specialized time-variant branch enhances control robustness in complex manipulation scenarios.
Summary by alphaXiv.
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