HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning

• URL: http://arxiv.org/abs/2604.06067v1
• Date: Tue, 07 Apr 2026 16:47:38 GMT
• Title: HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
• Abstract summary: HiPolicy is a hierarchical multi-frequency action chunking framework that predicts action sequences at different frequencies to capture both coarse high-level plans and precise reactive motions.<n> Experiments on diverse simulated benchmarks and real-world manipulation tasks show that HiPolicy can be seamlessly integrated into existing 2D and 3D generative policies.
• Score: 17.3424491065084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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