FLASH: Efficient Visuomotor Policy via Sparse Sampling
Generative models such as diffusion and flow matching have become dominant paradigms for visuomotor policy learning, yet their reliance on iterative denoising incurs high inference latency incompatible with real-time robotic control. We present Fast Legendre-polynomial Action policy via Sparse History-anchored flow (FLASH Policy), which replaces discrete action-chunk generation with continuous Legendre polynomial trajectory representation. Specifically, by fitting expert demonstrations under sparse temporal sampling, FLASH enables a single inference to cover a significantly extended action horizon. To further accelerate generation, FLASH initiates the flow matching process from history po...
| Authors | Jiaqi Bai, Jindou Jia, Yuxuan Hu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 May 2026 |
| Last updated at source | 5 October 2026 |
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