用训练时模拟延迟取代推理时补全，让机器人实时控制更快更省资源。
Training-Time Action Conditioning for Efficient Real-Time Chunking
- 训练时直接用动作前缀作条件，避免推理时补全的计算开销。
- 在真实任务中保持与原方法相当的性能和速度，且更高效。
- 无需改模型或运行时代码，几行代码即可实现，适合工程落地。
实时分块（RTC）使视觉-语言-动作模型（VLAs）能异步预测动作块，并通过推理时补全先前已执行的动作来实现平滑、响应迅速的机器人轨迹生成。然而，这种补全方法引入了计算开销，导致推理延迟增加。本文提出一种简单替代方案：在训练时模拟推理延迟，直接以动作前缀为条件，从而消除推理时的额外开销。该方法无需修改模型架构或机器人运行时，仅需少量代码即可实现。在仿真实验中，训练时RTC在较高推理延迟下表现优于推理时RTC。在真实世界中的盒子搭建和意式浓缩咖啡制作任务中，使用$π_{0.6}$ VLA进行测试，训练时RTC在保持任务性能和速度与原方法一致的同时，计算成本更低。结果表明，训练时动作条件是推理时补全在实时机器人控制中的实用替代方案。
原文摘要 · Abstract (English)
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 $π_{0.6}$ 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.
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