Action chunking has become a common inference strategy for flow-based robot policies, improving action coherence by modeling multi-step temporal dependencies in demonstrations. However, the execution horizon is still typically set as an empirical fixed value, overlooking that predictable free-space motions and precision-critical interaction phases often require different replanning frequencies. In this work, we first show that the denoising process of flow-based policies contains an intrinsic signal of task phases: clean-action estimates remain stable during predictable motion phases, but fluctuate more strongly around contact-rich or precision-sensitive operations. Motivated by this observation, we propose DVAC (Denoising-Variance Adaptive Chunking), a test-time method that adaptively determines how many actions to execute from each predicted chunk. DVAC measures the variance of clean-action estimates over the final denoising steps, executes the stable low-variance prefix, and replans before high-variance future actions are committed. To transfer across tasks and rollouts, DVAC further calibrates the threshold with a rolling estimate of the local variance scale. Experiments on LIBERO, RoboTwin, CALVIN, and real-world manipulation show that DVAC improves task success while reducing replanning frequency. With a $π_{0.5}$-based policy, DVAC improves LIBERO success from 94.75% to 98.00% and reduces replanning by 43.0%, while also yielding aggregate gains on RoboTwin and CALVIN and improving real-world execution efficiency.

翻译：动作分块已成为流式机器人策略的常见推理策略，通过建模演示中的多步时间依赖关系来提升动作连贯性。然而，执行时长通常仍被设置为经验固定值，忽略了可预测的自由空间运动与精度关键型交互阶段往往需要不同的重新规划频率。本文首先证明，流式策略的去噪过程包含任务阶段的内在信号：在可预测运动阶段，干净动作估计保持稳定，而在接触密集或精度敏感操作周围波动更为剧烈。受此观察启发，我们提出DVAC（去噪方差自适应分块），一种在测试时自适应决定从每个预测分块中执行多少动作的方法。DVAC测量最终去噪步骤中干净动作估计的方差，执行稳定的低方差前缀部分，并在高方差未来动作执行前重新规划。为跨任务和 rollout 迁移，DVAC 进一步利用局部方差尺度的滚动估计来校准阈值。在LIBERO、RoboTwin、CALVIN以及真实世界操控任务上的实验表明，DVAC在提升任务成功率的同时降低了重新规划频率。基于$π_{0.5}$策略，DVAC将LIBERO成功率从94.75%提升至98.00%，重新规划减少43.0%，同时在RoboTwin和CALVIN上取得了总体增益，并提升了真实世界执行效率。