High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching
arXiv:2607.03865v1 Announce Type: cross 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. Re
The continuous drive to reduce latency and improve fidelity in generative models for robotic control is a critical bottleneck for real-world deployment.
This development could significantly accelerate the practical application of visuomotor policies in robotics by overcoming key technical hurdles related to inference speed and accuracy.
Existing generative visuomotor policies will see improved real-time performance and reliability, making complex robotic tasks more feasible and robust.
- · Robotics companies
- · Automation sector
- · Generative AI researchers
- · Manufacturing industries
- · Companies with high-latency robotic systems
- · Legacy automation methods
Robots will become more agile and responsive in dynamic, unstructured environments.
This improved capability will expand the range of tasks robots can perform autonomously, impacting various industries from logistics to healthcare.
Increased robot autonomy could lead to shifts in labor markets and necessitate new regulatory frameworks for human-robot interaction.
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Read at arXiv cs.AI