Action-Consistent Attention with History Flow Matching

Generative policies based on flow matching have achieved remarkable success in robotics by formulating action-chunk prediction as a conditional probability path over a sequence of time-steps. However, when predicting the conditional velocity field, existing methods typically treat the action chunk as a flattened vector sequence, overlooking two fundamental semantic structures: the temporal evolution of each individual joint across consecutive steps, and the inter-joint coordination required within each time-step for physically coherent control. In this paper, we propose AC-HFlow (Action-Consistent History Flow Matching), a novel paradigm that reduces multi-step inference to a few integration steps, with as few as one, without sacrificing performance, while capturing coherent action semantics. It employs Action-Consistent Attention (ACA) to explicitly reason over the intra-chunk joint–time grid, capturing the partial order of joint evolution across timestamps and the directed coupling among joints within the same step. Meanwhile, pooled historical action statistics serve as an informative source for flow matching, which mitigates the overhead introduced by random-noise initialization. Extensive experiments demonstrate that AC-HFlow delivers superior action consistency, strong generalization capability under visual and scene shifts, and effective few-step inference in which a single integration step already attains competitive success.