HiPolicy: 階層型マルチ周波数アクションチャンキングによるポリシー学習
HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
ロボットの模倣学習において、長期的な依存関係のモデリングと細かい閉ループ制御のトレードオフを解決するため、異なる周波数でアクション系列を予測する階層型マルチ周波数アクションチャンキングフレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Jiyao Zhang, Zimu Han, Junhan Wang, Xionghao Wu, Shihong Lin, Jinzhou Li, Hongwei Fan, Ruihai Wu, Dongjiang Li, Hao Dong
分類: cs.RO
原文アブストラクト
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.
