HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning

Robotic imitation learning faces a fundamental trade-off be-tween modeling long-horizon dependencies and enabling fine-grained closed-loop control. Existing fixed-frequency action chunking approaches strug-gle to achieve both. Building on this insight, we propose HiPolicy, ahierarchical multi-frequency action chunking framework that jointly pre-dicts action sequences at different frequencies to capture both coarsehigh-level plans and precise reactive motions. We extract and fuse hi-erarchical features from history observations aligned to each frequencyfor multi-frequency chunk generation, and introduce an entropy-guidedexecution mechanism that adaptively balances long-horizon planningwith fine-grained control based on action uncertainty. Experiments ondiverse simulated benchmarks and real-world manipulation tasks showthat HiPolicy can be seamlessly integrated into existing 2D and 3D gen-erative policies, delivering consistent improvements in performance whilesignificantly enhancing execution efficiency.