High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching

• URL: http://arxiv.org/abs/2607.03865v1
• Date: Sat, 04 Jul 2026 13:12:38 GMT
• Title: High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching
• Abstract summary: Generative models such as diffusion and flow matching have advanced robotic visuomotor policies by modeling multimodal action distributions.<n>Existing one-step acceleration methods compress the whole generation process into a single large update, leading to spatial deviation, frequency distortion, and mode averaging.<n>This paper proposes a high-fidelity one-step generative visuomotor policy framework that addresses these issues with three complementary mechanisms.
• Score: 3.5570904967011505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models such as diffusion and flow matching have advanced robotic visuomotor policies by modeling multimodal action distributions, but their multi-step sampling or ODE solving introduces inference latency. Existing one-step acceleration methods often compress the whole generation process into a single large update, leading to spatial deviation, frequency distortion, and mode averaging. This paper proposes a high-fidelity one-step generative visuomotor policy framework that addresses these issues with three complementary mechanisms. Recursive Consistent Action Flow (RCAF) uses recursive correction to compensate for spatial truncation errors and align one-step predictions with refined flow trajectories. Dual-Timestep Frequency Consistency (DTFC) preserves high-frequency manipulation details through adaptive spectral consistency across flow timesteps. Contrastive Flow Matching (CFM) separates entangled action flows with a margin-based repulsive objective, reducing ambiguous actions in multimodal manipulation. Experiments on RoboTwin, RoboTwin 2.0, Adroit, DexArt, and real-world robot platforms show that the proposed method achieves competitive or superior performance compared with strong 10-step generative policy baselines while requiring only one forward pass (1 NFE), enabling low-latency visuomotor control.

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