One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies

Generative flow and diffusion models provide the continu-ous, multimodal action distributions needed for high-precision roboticpolicies. However, their reliance on iterative sampling introduces severeinference latency, degrading control frequency and harming performancein time-sensitive manipulation. To address this problem, we proposethe One-Step Flow Policy (OFP), a from-scratch self-distillation frame-work for high-fidelity, single-step action generation without a pre-trainedteacher. OFP unifies a self-consistency loss to enforce coherent transportacross time intervals, and a self-guided regularization to sharpen predic-tions toward high-density expert modes. In addition, a warm-start mech-anism leverages temporal action correlations to minimize the generativetransport distance. Evaluations across 56 diverse simulated manipulationtasks demonstrate that a one-step OFP achieves state-of-the-art results,outperforming 100-step diffusion and flow policies while accelerating ac-tion generation by over 100×. We further integrate OFP into the π0.5model on RoboTwin 2.0, where one-step OFP surpasses the original 10-step policy. These results establish OFP as a practical, scalable solutionfor highly accurate and low-latency robot control.