Real-time execution is essential for cyber-physical systems such as robots. These systems operate in dynamic real-world environments where even small delays can undermine responsiveness and compromise performance. Asynchronous inference has recently emerged as a system-level paradigm for real-time robot manipulation, enabling the next action chunk to be predicted while the current one is being executed. While this approach achieves real-time responsiveness, naive integration often results in execution failure. Previous methods attributed this failure to inter-chunk discontinuity and developed test-time algorithms to smooth chunk boundaries. In contrast, we identify another critical yet overlooked factor: intra-chunk inconsistency, where the robot's executed action chunk partially misaligns with its current perception. To address this, we propose REMAC, which learns corrective adjustments on the pretrained policy through masked action chunking, enabling the policy to remain resilient under mismatches between intended actions and actual execution during asynchronous inference. In addition, we introduce a prefix-preserved sampling procedure to reinforce inter-chunk continuity. Overall, our method delivers more reliable policies without incurring additional latency. Extensive experiments in both simulation and real-world settings demonstrate that our method enables faster task execution, maintains robustness across varying delays, and consistently achieves higher completion rates.

翻译：实时执行对于机器人等网络物理系统至关重要。这些系统在动态的现实世界环境中运行，即使微小的延迟也可能削弱响应能力并影响性能。异步推理最近已成为实现实时机器人操作的系统级范式，它能够在执行当前动作块的同时预测下一个动作块。尽管这种方法实现了实时响应，但简单的集成往往会导致执行失败。先前的方法将这种失败归因于块间不连续性，并开发了测试时算法来平滑块间边界。与此相反，我们发现另一个关键但被忽视的因素：块内不一致性，即机器人执行的动作块与其当前感知部分不匹配。为解决这一问题，我们提出REMAC方法，该方法通过掩码动作分块在预训练策略上学习校正调整，使策略在异步推理期间能够在意向动作与实际执行不匹配时保持鲁棒性。此外，我们引入了一种前缀保持采样程序以增强块间连续性。总体而言，我们的方法在不引入额外延迟的情况下提供了更可靠的策略。在仿真和真实环境中的大量实验表明，我们的方法能够实现更快的任务执行，在不同延迟下保持鲁棒性，并持续获得更高的任务完成率。