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arXiv AI By Zhenchen Dong, Fulin Chen, Jinna Fu, Jiaming Wu, Qingran Wu, Shengyuan Yu, Hongyu Yu, Yide Liu

HybridFlow: A 2-NFE Generative Policy for Real-Time Robotic Manipulation

Read the original on arXiv AI →

HybridFlow is a generative policy for robotic manipulation that uses a three‑stage inference procedure requiring only two network function evaluations (2‑NFE). The policy first generates a coarse action trajectory with a Global Jump based on MeanFlow, then refines the state using a parameter‑free ReNoise interpolation, and finally performs a Local Refine to query the instantaneous‑velocity limit. Experiments on RoboMimic and five real‑robot settings show that HybridFlow achieves high success rates and improves task performance over a 16‑step Diffusion Policy while reducing action‑generation latency by roughly eightfold.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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