HybridFlow: A Two-Step Generative Policy for Robotic Manipulation

TL;DR

HybridFlow is a novel three-stage, low-latency robotic manipulation policy that balances speed and precision, significantly outperforming existing diffusion-based methods in success rate and inference time.

Contribution

It introduces HybridFlow, a three-stage generative policy combining MeanFlow and ReFlow modes, achieving fast and precise robot actions with minimal inference steps.

Findings

Outperforms 16-step Diffusion Policy by 15-25% success rate

Reduces inference time from 152ms to 19ms (8x speedup)

Achieves 70.0% success on unseen-color OOD grasping

Abstract

Limited by inference latency, existing robot manipulation policies lack sufficient real-time interaction capability with the environment. Although faster generation methods such as flow matching are gradually replacing diffusion methods, researchers are pursuing even faster generation suitable for interactive robot control. MeanFlow, as a one-step variant of flow matching, has shown strong potential in image generation, but its precision in action generation does not meet the stringent requirements of robotic manipulation. We therefore propose \textbf{HybridFlow}, a \textbf{3-stage method} with \textbf{2-NFE}: Global Jump in MeanFlow mode, ReNoise for distribution alignment, and Local Refine in ReFlow mode. This method balances inference speed and generation quality by leveraging the rapid advantage of MeanFlow one-step generation while ensuring action precision with minimal generation steps. Through real-world experiments, HybridFlow outperforms the 16-step Diffusion Policy by \textbf{15--25\%} in success rate while reducing inference time from 152ms to 19ms (\textbf{8$\times$ speedup}, \textbf{$\sim$52Hz}); it also achieves 70.0\% success on unseen-color OOD grasping and 66.3\% on deformable object folding. We envision HybridFlow as a practical low-latency method to enhance real-world interaction capabilities of robotic manipulation policies.