HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914454078226432 |
|---|---|
| author | Zhang, Jiyao Han, Zimu Wang, Junhan Wu, Xionghao Lin, Shihong Li, Jinzhou Fan, Hongwei Wu, Ruihai Li, Dongjiang Dong, Hao |
| author_facet | Zhang, Jiyao Han, Zimu Wang, Junhan Wu, Xionghao Lin, Shihong Li, Jinzhou Fan, Hongwei Wu, Ruihai Li, Dongjiang Dong, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06067 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning Zhang, Jiyao Han, Zimu Wang, Junhan Wu, Xionghao Lin, Shihong Li, Jinzhou Fan, Hongwei Wu, Ruihai Li, Dongjiang Dong, Hao Robotics 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. |
| title | HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.06067 |