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arXiv · 2604.06067

HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning

Jiyao Zhang·Zimu Han·Junhan Wang·Xionghao Wu·Shihong Lin·Jinzhou Li·Hongwei Fan·Ruihai Wu·Dongjiang Li·Hao Dong
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Abstract

Robotic imitation learning faces a fundamental trade-off between modeling long-horizon dependencies and enabling fine-grained closed-loop control. Existing fixed-frequency action chunking approaches struggle to achieve both. Building on this insight, we propose HiPolicy, a hierarchical multi-frequency action chunking framework that jointly predicts action sequences at different frequencies to capture both coarse high-level plans and precise reactive motions. We extract and fuse hierarchical features from history observations aligned to each frequency for multi-frequency chunk generation, and introduce an entropy-guided execution mechanism that adaptively balances long-horizon planning with fine-grained control based on action uncertainty. Experiments on diverse simulated benchmarks and real-world manipulation tasks show that HiPolicy can be seamlessly integrated into existing 2D and 3D generative policies, delivering consistent improvements in performance while significantly enhancing execution efficiency.

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Jiyao Zhang, Zimu Han, Junhan Wang, Xionghao Wu, Shihong Lin, Jinzhou Li, Hongwei Fan, Ruihai Wu, Dongjiang Li, Hao Dong. 2026-04-07. HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning. https://arxiv.org/abs/2604.06067

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