One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies
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 unifies a self-consistency loss to enforce coherent transport across time intervals, and a self-guided regularization to sharpen predictions toward high-density expert modes. In addition, a warm-sta...
| Authors | Shaolong Li, Lichao Sun, Yongchao Chen |
|---|---|
| Primary category | cs.RO |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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