In Vision-Language-Action (VLA) models, action chunking (i.e., executing a sequence of actions without intermediate replanning) is a key technique to improve robotic manipulation abilities. However, a large chunk size reduces the model's responsiveness to new information, while a small one increases the likelihood of mode-jumping, jerky behavior resulting from discontinuities between chunks. Therefore, selecting the optimal chunk size is an urgent demand to balance the model's reactivity and consistency. Unfortunately, a dominant trend in current VLA models is an empirical fixed chunk length at inference-time, hindering their superiority and scalability across diverse manipulation tasks. To address this issue, we propose a novel Adaptive Action Chunking (AAC) strategy, which exploits action entropy as the cue to adaptively determine the chunk size based on current predictions. Extensive experiments on a wide range of simulated and real-world robotic manipulation tasks have demonstrated that our approach substantially improves performance over the state-of-the-art alternatives. The videos and source code are publicly available at https://lance-lot.github.io/adaptive-chunking.github.io/.

翻译：在视觉-语言-动作（VLA）模型中，动作分块（即在无中间重新规划的情况下执行一系列动作）是提升机器人操作能力的关键技术。然而，分块过大会降低模型对新信息的响应能力，分块过小则容易引发模式跳跃，即因分块间不连续性导致的抖振行为。因此，选择最优分块尺寸成为平衡模型响应性与一致性的迫切需求。遗憾的是，当前VLA模型的主流趋势是在推理阶段采用经验性固定分块长度，这限制了其在不同操作任务中的优越性和可扩展性。为解决此问题，我们提出了一种新型自适应动作分块（AAC）策略，该策略利用动作熵作为线索，根据当前预测自适应确定分块尺寸。在广泛模拟和真实机器人操作任务上的大量实验表明，我们的方法较现有最优方案显著提升了性能。相关视频和源代码已开源，可访问 https://lance-lot.github.io/adaptive-chunking.github.io/。