分层多频动作分块，让机器人同时规划长程任务与精细控制。
HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
- 分层设计多频率动作块，融合不同频率的历史观测特征。
- 在仿真与真实任务中提升执行效率，性能稳定优于基线方法。
- 适合需兼顾长期规划与实时反应的机器人控制场景。
机器人模仿学习面临建模长时依赖与实现细粒度闭环控制之间的根本矛盾。现有固定频率动作分块方法难以兼顾两者。为此，我们提出HiPolicy，一种分层多频动作分块框架，通过联合预测不同频率下的动作序列，同时捕捉高层粗略规划与底层精确反应动作。该方法从对齐各频率的历史观测中提取并融合层次化特征，用于生成多频动作块，并引入基于熵的执行机制，根据动作不确定性自适应平衡长时规划与细粒度控制。在多种仿真基准和真实世界操作任务上的实验表明，HiPolicy可无缝集成至现有的2D与3D生成式策略中，在保持性能一致提升的同时显著提高执行效率。
原文摘要 · Abstract (English)
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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