KoopmanFlow: Spectrally Decoupled Generative Control Policy via Koopman Structural Bias
This work addresses the challenge that existing generative control policies struggle to simultaneously maintain stable global motion and perform high-frequency local corrections, as unified time integration often smooths out transient details. To overcome this, the authors propose a spectrally decoupled generative control architecture that incorporates a Koopman structural prior within a unified multimodal latent space. The macro branch models slowly varying trajectories via single-step consistency training, while the transient branch captures high-frequency residuals induced by visual discontinuities—such as contacts or occlusions—using flow matching. An asymmetric consistency objective enables joint modeling of low- and high-frequency dynamics. This approach avoids error accumulation across multiple stages and significantly outperforms current methods in contact-rich, disturbance-sensitive tasks, achieving both high control accuracy and parameter efficiency under real-time deployment constraints.