Real-Time Execution of Action Chunking Flow Policies
Not on the map. Why?
Cited by 118 in Vision-language-action models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Modern AI systems, especially those interacting with the physical world, increasingly require real-time performance. However, the high latency of state-of-the-art generalist models, including recent vision-language action models (VLAs), poses a significant challenge. While action chunking has enabled temporal consistency in high-frequency control tasks, it does not fully address the latency problem, leading to pauses or out-of-distribution jerky movements at chunk boundaries. This paper presents a novel inference-time algorithm that enables smooth asynchronous execution of action chunking policies. Our method, real-time chunking (RTC), is applicable to any diffusion- or flow-based VLA out of the box with no re-training. It generates the next action chunk while executing the current one, "freezing" actions guaranteed to execute and "inpainting" the rest. To test RTC, we introduce a new benchmark of 12 highly dynamic tasks in the Kinetix simulator, as well as evaluate 6 challenging real-world bimanual manipulation tasks. Results demonstrate that RTC is fast, performant, and uniquely robust to inference delay, significantly improving task throughput and enabling high success rates in precise tasks unicodex2013 such as lighting a match unicodex2013 even in the presence of significant latency. See https://pi.website/research/realtimechunking for videos.
Key facts. Cited by 216 papers in the map (data through 14 Oct 2026). Builds on 54 of its 70 references that are in the map. First posted on arXiv 9 Jun 2025.
Builds on
54 of 70 references.
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50 most cited of 54 All 54 (JSON)
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Citing papers per year: 2025: 29; 2026: 187.
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