Abstract
Real-time chunking (RTC) enables vision-language-action models (VLAs) to generate smooth, reactive robot trajectories by asynchronously predicting action chunks and conditioning on previously committed actions via inference-time inpainting. However, this inpainting method introduces computational overhead that increases inference latency. In this work, we propose a simple alternative: simulating inference delay at training time and conditioning on action prefixes directly, eliminating any inference-time overhead. Our method requires no modifications to the model architecture or robot runtime, and can be implemented with only a few additional lines of code. In simulated experiments, we find that training-time RTC outperforms inference-time RTC at higher inference delays. In real-world experiments on box building and espresso making tasks with the $\pi_{0.6}$ VLA, we demonstrate that training-time RTC maintains both task performance and speed parity with inference-time RTC while being computationally cheaper. Our results suggest that training-time action conditioning is a practical drop-in replacement for inference-time inpainting in real-time robot control.
Abstract (translated)
实时分块（RTC）技术使视觉-语言-行动模型（VLAs）能够通过异步预测动作片段并利用推理时的补帧方法参考之前已确定的动作，生成流畅且响应迅速的机器人轨迹。然而，这种补帧方式引入了计算开销，增加了推断延迟。在这项工作中，我们提出了一种简单的替代方案：在训练阶段模拟推理延迟，并直接基于行动前缀进行条件设置，从而消除了所有推断时的额外开销。我们的方法不需要对模型架构或机器人运行时间进行任何修改，仅需添加少量代码即可实现。在仿真实验中，我们发现训练时的RTC优于推理时的RTC，在更高的推断延迟下表现更佳。在现实世界中的盒子构建和意式咖啡制作任务上使用$\pi_{0.6}$ VLA模型的真实实验表明，训练时的RTC不仅保持了与推断时RTC相同的任务性能和速度，并且计算成本更低。我们的结果表明，基于训练阶段的动作条件设置可以作为实时机器人控制中推理时补帧的有效替代方案。
URL
https://arxiv.org/abs/2512.05964
