2026-05-18 04:00 UTCOriginal source2 min readUpdated: 2026-06-30 13:03 UTC

FLASH: Efficient Visuomotor Policy via Sparse Sampling

Researchers introduce FLASH Policy, a new visuomotor policy that uses Legendre polynomial trajectory representation and sparse temporal sampling to enable single-step inference, achieving state-of-the-art success rates and dramatically reducing inference time compared to diffusion and flow matching policies.

SourcearXiv RoboticsAuthor: Jiaqi Bai, Jindou Jia, Yuxuan Hu, Gen Li, Xiangyu Chen, Tuo An, Kuangji Zuo, Jianfei Yang

[2605.15492] FLASH: Efficient Visuomotor Policy via Sparse Sampling

[Submitted on 15 May 2026]

Title:FLASH: Efficient Visuomotor Policy via Sparse Sampling

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Abstract: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 polynomial coefficients rather than uninformative Gaussian noise, shortening the transport distance and enabling accurate single-step inference. Moreover, analytic polynomial differentiation directly provides desired velocity feed-forward signals to the torque controller without numerical approximation. Extensive experiments on five simulated and two real-world manipulation tasks demonstrate that FLASH achieves state-of-the-art success rates ($\ge 92\%$ across all tasks), a per-episode inference time of $31.40\,ms$ (up to $175\times$ faster than diffusion policies and $18\times$ faster than prior flow matching policies), up to $4\times$ faster training convergence than ACT, and $5\times$ to $7\times$ reduction in controller tracking error compared to discrete-action baselines.

Comments: 19 pages, 10 figures

Subjects:

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2605.15492 [cs.RO]

(or arXiv:2605.15492v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.15492

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jiaqi Bai [view email] [v1] Fri, 15 May 2026 00:15:24 UTC (7,363 KB)

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