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arXiv:cs.AI· Houlong Xiong, Zhenqi Qiu, Zechen Wang, Suohang Zhang, Yiyu Ren, Wanting Xu, Hongfei Niu, Chengyang He, Ge Sun, Ran Cheng, Qian Zhu·· 4 小时前

REACT:用滚动去噪与双重解耦提升 VLA 模型的机器人反应式控制

REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models

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研究者提出 REACT 滚动去噪框架,让基于 flow 的 VLA 模型在保留长时序上下文的同时更具反应性:它维护带错位 flow 时间步的持久动作缓冲区,每步用最新观测对完整时域去噪,执行最干净的动作块,并将其余部分前移、尾部追加新噪声。

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Authors:Houlong Xiong, Zhenqi Qiu, Zechen Wang, Suohang Zhang, Yiyu Ren, Wanting Xu, Hongfei Niu, Chengyang He, Ge Sun, Ran Cheng, Qian Zhu

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Abstract:Flow-based vision-language-action (VLA) models generate action chunks for temporally coherent robot motion, but chunked control creates a fundamental closed-loop trade-off: long chunks provide smooth execution, whereas frequent replanning improves reactivity at the cost of action discontinuities. We introduce REACT, a rolling-denoising framework that makes flow-based VLAs more reactive while preserving long-horizon context. Instead of regenerating entire action chunks from scratch, REACT maintains a persistent action buffer with staggered flow timesteps. At each control step, the full horizon is denoised using the latest observation, the cleanest action block is executed, partially refined future blocks are shifted forward, and fresh noise is appended to the tail. As a result, each executed action block is refined across multiple recent observations before deployment. To support real-time control, we further introduce dual decoupling, which separates sensing, VLM encoding, DiT denoising, and action execution, enabling high-frequency observation updates and action streaming under practical compute constraints. Across the RoboTwin 2.0 simulation benchmark and real-world tasks spanning bimanual manipulation and dynamic control on multiple robot platforms, REACT improves task success and reduces reaction latency while producing smoother trajectories than frequent-replanning and asynchronous baselines.
Comments: Accepted to CoRL 2026 as Spotlight. Project page: this https URL
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.12007 [cs.RO]
  (or arXiv:2610.12007v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2610.12007

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Houlong Xiong [view email]
[v1] Thu, 8 Oct 2026 14:09:46 UTC (16,529 KB)

来源:arXiv:cs.AI · arxiv.org