Achieves 100x speedup in robotic action generation by distilling iterative flow/diffusion models into a one-step policy without a pre-trained teacher.
arXiv · March 16, 2026 · 2603.12480
Why it matters
Iterative sampling in diffusion policies creates high latency, making them impractical for real-time, high-frequency robot control. This self-distillation framework enables single-step inference with state-of-the-art accuracy, removing the primary bottleneck for deploying generative policies in robotics.
From the abstract
Generative flow and diffusion models provide the continuous, multimodal action distributions needed for high-precision robotic policies. However, their reliance on iterative sampling introduces severe inference latency, degrading control frequency and harming performance in time-sensitive manipulation. To address this problem, we propose the One-Step Flow Policy (OFP), a from-scratch self-distillation framework for high-fidelity, single-step action generation without a pre-trained teacher. OFP u