The Missing Temporal Dimension in Flow-Based Action Chunking

Action chunking has emerged as a cornerstone technique in robotic control and imitation learning. Recent robotic manipulation policies predominantly employ action-chunked policies trained with flow matching objectives, achieving substantial performance gains. Despite this success, existing approaches represent action chunks merely as unstructured vectors during flow-based generation, overlooking the physical-time dependencies among actions within each chunk and potentially leading to ineffective policy learning. In this paper, we show that additionally supervising the first-order temporal derivatives of actions within a chunk provides a surprisingly effective way to incorporate physical-time dependencies into flow-based policies. We further establish that this supervision scheme corresponds to a special case of path-independent flow matching, in which action chunk generation evolves jointly along two dimensions: denoising time and physical time. By capturing the joint effects of multi-step denoising and the physical-time evolution of actions, our method enables fully exploiting the rich temporal structure of action chunks while requiring only one additional flow-based loss term. Across a broad range of simulation benchmarks and real-robot experiments, our method consistently improves both performance and sample efficiency over the base policy, with particularly pronounced gains on real-robot tasks. These results highlight the importance of explicitly modeling the temporal structure inherent in action chunking.