Predicting and executing a sequence of actions without intermediate replanning, known as action chunking, is increasingly used in robot learning from human demonstrations. However, its effects on learned policies remain puzzling: some studies highlight its importance for achieving strong performance, while others observe detrimental effects. In this paper, we first dissect the role of action chunking by analyzing the divergence between the learner and the demonstrator. We find that longer action chunks enable a policy to better capture temporal dependencies by taking into account more past states and actions within the chunk. However, this advantage comes at the cost of exacerbating errors in stochastic environments due to fewer observations of recent states. To address this, we propose Bidirectional Decoding (BID), a test-time inference algorithm that bridges action chunking with closed-loop operations. BID samples multiple predictions at each time step and searches for the optimal one based on two criteria: (i) backward coherence, which favors samples aligned with previous decisions, (ii) forward contrast, which favors samples close to outputs of a stronger policy and distant from those of a weaker policy. By coupling decisions within and across action chunks, BID enhances temporal consistency over extended sequences while enabling adaptive replanning in stochastic environments. Experimental results show that BID substantially outperforms conventional closed-loop operations of two state-of-the-art generative policies across seven simulation benchmarks and two real-world tasks.

本文探讨了动作分块在机器人学习中的作用，尤其是在学习者与演示者之间的差异。我们提出了一种新颖的双向解码算法，结合了动作分块和闭环操作，以增强时间一致性，并在随机环境中促进自适应重规划。实验结果表明，该方法在模拟和真实任务中显著优于现有的生成策略。

双向解码：通过闭环重采样改善动作分块