Real-Time Execution of Action Chunking Flow Policies
Kevin Black, Manuel Y. Galliker, Sergey Levine

TL;DR
This paper introduces RTC, a novel inference-time algorithm that enables real-time, smooth, asynchronous execution of action chunking policies in AI systems, significantly reducing latency and improving performance in dynamic tasks.
Contribution
RTC is a new inference-time method that allows any diffusion- or flow-based VLA to run asynchronously without re-training, enhancing real-time performance and robustness.
Findings
RTC improves task throughput in dynamic environments.
RTC maintains high success rates despite inference delays.
RTC is effective in both simulated and real-world manipulation tasks.
Abstract
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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Multimodal Machine Learning Applications · Robot Manipulation and Learning
