FLASH: Efficient Visuomotor Policy via Sparse Sampling

• URL: http://arxiv.org/abs/2605.15492v1
• Date: Fri, 15 May 2026 00:15:24 GMT
• Title: FLASH: Efficient Visuomotor Policy via Sparse Sampling
• Abstract summary: We present Fast Legendre-polynomial Action policy via Sparse History-anchored flow.<n>FLASH Policy replaces discrete action-chunk generation with continuous Legendre trajectory representation.
• Score: 25.25550374813956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching [3.5570904967011505]
  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.
  arXiv  Detail & Related papers  (2026-07-04T13:12:38Z)
• Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control [51.92884966472683]
  MSCoT is a multi-scale, coarse-to-fine model for test-time human motion synthesis and control.<n> MSCoT discretizes motion into a multi-scale hierarchical representation and predicts the entire token sequence at each temporal scale.
  arXiv  Detail & Related papers  (2026-05-14T15:09:49Z)
• ScoRe-Flow: Complete Distributional Control via Score-Based Reinforcement Learning for Flow Matching [14.421249343302028]
  Flow Matching (FM) policies have emerged as an efficient backbone for robotic control.<n>We propose ScoRe-Flow, a score-based RL fine-tuning method to achieve decoupled control over the mean and variance of transitions.
  arXiv  Detail & Related papers  (2026-04-13T03:56:37Z)
• One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies [18.743330791557522]
  One-Step Flow Policy (OFP) is a from-scratch self-distillation framework for high-fidelity, single-step action generation without a pre-trained teacher.<n> Evaluations across 56 diverse simulated manipulation tasks demonstrate that a one-step OFP achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2026-03-12T21:58:12Z)
• Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation [53.750389076941396]
  Diffusion-based visuomotor policies capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control.<n>We propose Ada3Drift, which learns a training-time drifting field that attracts predicted actions toward expert demonstration modes.<n>Ada3Drift achieves state-of-the-art performance while requiring $10times$ fewer function evaluations than diffusion-based alternatives.
  arXiv  Detail & Related papers  (2026-03-12T14:36:53Z)
• Riemannian MeanFlow for One-Step Generation on Manifolds [54.09734511705173]
  Flow Matching enables simulation-free training of generative models.<n>MeanFlow can be extended to manifold-valued generation where velocities lie in location-dependent tangent spaces.<n>Experiments on spheres, tori, and SO(3) demonstrate competitive one-step sampling with improved quality-efficiency trade-offs and substantially reduced sampling cost.
  arXiv  Detail & Related papers  (2026-03-11T12:41:46Z)
• From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation [18.70033095161235]
  We propose a framework that distills a Conditional Flow Matching expert into a fast single-step student via Implicit Likelihood Estimation (IMLE)<n>A bi-directional Chamfer distance provides a set-level objective that promotes both mode coverage and fidelity.<n>A unified perception encoder further integrates multi-view RGB, depth, point clouds, and proprioception into a geometry-aware representation.
  arXiv  Detail & Related papers  (2026-03-10T09:30:05Z)
• FlowConsist: Make Your Flow Consistent with Real Trajectory [99.22869983378062]
  We argue that current fast-flow training paradigms suffer from two fundamental issues.<n> conditional velocities constructed from randomly paired noise-data samples introduce systematic trajectory drift.<n>We propose FlowConsist, a training framework designed to enforce trajectory consistency in fast flows.
  arXiv  Detail & Related papers  (2026-02-06T03:24:23Z)
• PRISM: Performer RS-IMLE for Single-pass Multisensory Imitation Learning [51.24484551729328]
  We introduce PRISM, a single-pass policy based on a batch-global rejection-sampling variant of IMLE.<n> PRISM couples a temporal multisensory encoder with a linear-attention generator using a Performer architecture.<n>We demonstrate the efficacy of PRISM on a diverse real-world hardware suite, including loco-manipulation using a Unitree Go2 with a 7-DoF arm D1 and tabletop manipulation with a UR5 manipulator.
  arXiv  Detail & Related papers  (2026-02-02T17:57:37Z)
• ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning [18.435889278351297]
  ESPADA is a semantically aware framework that segments demonstrations using a VLM-LLM pipeline with 3D gripper-object relations.<n>To scale from a single annotated episode to the full dataset, ESPADA propagates segment labels via Dynamic Time Warping.<n> ESPADA achieves approximately a 2x speed-up while maintaining success rates, narrowing the gap between human demonstrations and efficient robot control.
  arXiv  Detail & Related papers  (2025-12-08T10:08:33Z)
• Steering Vision-Language-Action Models as Anti-Exploration: A Test-Time Scaling Approach [78.4812458793128]
  We propose textbfTACO, a test-time-scaling framework that applies a lightweight pseudo-count estimator as a high-fidelity verifier of action chunks.<n>Our method resembles the classical anti-exploration principle in offline reinforcement learning (RL), and being gradient-free, it incurs significant computational benefits.
  arXiv  Detail & Related papers  (2025-12-02T14:42:54Z)
• One-Step Flow Policy Mirror Descent [52.31612487608593]
  Flow Policy Mirror Descent (FPMD) is an online RL algorithm that enables 1-step sampling during flow policy inference.<n>Our approach exploits a theoretical connection between the distribution variance and the discretization error of single-step sampling in straight flow matching models.
  arXiv  Detail & Related papers  (2025-07-31T15:51:10Z)
• Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories [28.01020980367346]
  We simplify diffusion$/$flow policies by treating action trajectories as flow trajectories.<n>Our algorithm samples from a narrow Gaussian around the last action.<n>It incrementally integrates a velocity field learned via flow matching to produce a sequence of actions that constitute a single trajectory.
  arXiv  Detail & Related papers  (2025-05-28T00:48:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.