FASTER: Rethinking Real-Time Flow VLAs

• URL: http://arxiv.org/abs/2603.19199v1
• Date: Thu, 19 Mar 2026 17:51:37 GMT
• Title: FASTER: Rethinking Real-Time Flow VLAs
• Abstract summary: Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world.<n>We show that reaction time follows a uniform distribution determined jointly by the Time to First Action (TTFA) and the execution horizon.<n>We propose Fast Action Sampling for ImmediaTE Reaction (FASTER) to overcome this issue.
• Score: 82.58822112377923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world. Existing asynchronous inference methods primarily optimize trajectory smoothness, but neglect the critical latency in reacting to environmental changes. By rethinking the notion of reaction in action chunking policies, this paper presents a systematic analysis of the factors governing reaction time. We show that reaction time follows a uniform distribution determined jointly by the Time to First Action (TTFA) and the execution horizon. Moreover, we reveal that the standard practice of applying a constant schedule in flow-based VLAs can be inefficient and forces the system to complete all sampling steps before any movement can start, forming the bottleneck in reaction latency. To overcome this issue, we propose Fast Action Sampling for ImmediaTE Reaction (FASTER). By introducing a Horizon-Aware Schedule, FASTER adaptively prioritizes near-term actions during flow sampling, compressing the denoising of the immediate reaction by tenfold (e.g., in $π_{0.5}$ and X-VLA) into a single step, while preserving the quality of long-horizon trajectory. Coupled with a streaming client-server pipeline, FASTER substantially reduces the effective reaction latency on real robots, especially when deployed on consumer-grade GPUs. Real-world experiments, including a highly dynamic table tennis task, prove that FASTER unlocks unprecedented real-time responsiveness for generalist policies, enabling rapid generation of accurate and smooth trajectories.

Related papers

• World Action Models in Real Time: An Empirical Study of Smooth Execution via Asynchronous Deployment [0.0]
  We study strategies that overlap model inference with action execution to enable responsive and smooth control.<n>We compare six strategies, including synchronous execution, pure asynchronous switching, post-hoc action blending, denoising-time blending, inference-time velocity guidance, and prefix-conditioned generation.
  arXiv  Detail & Related papers  (2026-08-03T08:24:36Z)
• FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking [51.97839311685636]
  FutureRTC is a plug-and-play adaptation framework that predicts execution-time observations and states for asynchronous VLA control without modifying the underlying policy.<n>We show that FutureRTC achieves robustness to inference delays, resulting in smoother trajectories, faster execution, and consistently higher task success rates.
  arXiv  Detail & Related papers  (2026-07-27T05:10:16Z)
• TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies [58.40033352838586]
  Existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations.<n>We observe that the magnitude of each predicted action already governs how fast the robot moves.<n>We turn this observation into TempoVLA, a single VLA whose execution speed is controlled by an explicit condition.
  arXiv  Detail & Related papers  (2026-06-04T17:59:40Z)
• ElasticFlow: One-Step Physics-Consistent Policy with Elastic Time Horizons for Language-Guided Manipulation [11.93174278125059]
  ElasticFlow is a distillation-free, physics-consistent one-step policy framework.<n>We reconstruct the Mean Field Theory by directly modeling the average velocity field.<n>We introduce the Elastic Time Horizons mechanism.
  arXiv  Detail & Related papers  (2026-05-09T08:45:03Z)
• Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation [91.20850436220267]
  We introduce the Referring-Aware Visuomotor Policy (ReV)<n>ReV incorporates sparse referring points provided by a human or a high-level reasoning planner.<n>It is trained only by applying targeted perturbations to expert demonstrations.
  arXiv  Detail & Related papers  (2026-04-07T07:41:11Z)
• F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation [62.06267255986041]
  Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation.<n>In this paper, we propose a novel framework that leverages predicted object flow to synthesize future observations.<n>Our approach significantly enhances responsiveness and success rates in complex dynamic manipulation tasks.
  arXiv  Detail & Related papers  (2026-04-02T17:57:15Z)
• ProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action Models [12.232846303286161]
  Recent Vision-Language-Action (VLA) models equipped with Flow Matching (FM) action heads achieve state-of-the-art performance in complex robot manipulation.<n>While current acceleration efforts optimize the Vision-Language Model (VLM) backbone, the action head bottleneck remains overlooked.<n>We propose ProbeFlow, a training-free adaptive inference framework for continuous robotic control.
  arXiv  Detail & Related papers  (2026-03-18T15:38:29Z)
• AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge [49.66156306240961]
  High latency breaks the control loop, rendering powerful models unsafe for real-time deployment.<n>We propose AsyncVLA, an asynchronous control framework that decouples semantic reasoning from reactive execution.<n>AsyncVLA achieves a 40% higher success rate than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-13T21:31:19Z)
• Real-Time Robot Execution with Masked Action Chunking [38.37108371991901]
  Real-time execution is essential for cyber-physical systems such as robots.<n>Asynchronous inference has recently emerged as a system-level paradigm for real-time robot manipulation.<n>We propose REMAC, which learns corrective adjustments on the pretrained policy through masked action chunking.
  arXiv  Detail & Related papers  (2026-01-27T23:48:32Z)
• VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots [5.308743386891208]
  Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics.<n>The strategies for fusing a queue of successive action chunks have a profound impact on the overall performance of VLA models.<n>Existing methods suffer from jitter, stalling, or even pauses in robotic action execution.<n>This paper introduces VLA-RAIL, a novel framework designed to conduct model inference and robot motion control asynchronously.
  arXiv  Detail & Related papers  (2025-12-31T06:59:42Z)
• ActionFlow: A Pipelined Action Acceleration for Vision Language Models on Edge [11.016302257907936]
  Vision-Language-Action (VLA) models have emerged as a unified paradigm for robotic perception and control.<n>Current VLA models operate at only 3-5 Hz on edge devices due to the memory bound nature of autoregressive decoding.<n>We introduce ActionFlow, a system-level inference framework tailored for resource-constrained edge plat forms.
  arXiv  Detail & Related papers  (2025-12-23T11:29:03Z)
• ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning [52.86018040861575]
  We propose a unified end-to-end visual-force diffusion policy that integrates visual planning and reactive force control within a single network.<n>We introduce Structural Slow-Fast Learning, a mechanism utilizing causal attention to simultaneously process asynchronous visual and force tokens.<n>Experiments on contact-rich tasks demonstrate that ImplicitRDP significantly outperforms both vision-only and hierarchical baselines.
  arXiv  Detail & Related papers  (2025-12-11T18:59:46Z)
• 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)
• FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization [61.10456021136654]
  We introduce FASTer, a unified framework for efficient and general robot learning.<n>FASTerVQ encodes action chunks as single-channel images, capturing global-temporal dependencies while maintaining a high compression ratio.<n>FASTerVLA builds on this tokenizer with block-wise autoregressive decoding and a lightweight action expert, achieving both faster inference and higher task performance.
  arXiv  Detail & Related papers  (2025-12-04T16:21:38Z)
• Real-Time Execution of Action Chunking Flow Policies [49.1574468325115]
  This paper presents a novel inference-time algorithm that enables asynchronous execution of action interacting systems.<n>It is applicable to any diffusion- or VLA-based systems executing out of the box with no re-training.<n>Results show that RTC is fast, performant, and uniquely robust to inference manipulation.
  arXiv  Detail & Related papers  (2025-06-09T01:01:59Z)

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.