Embodied3DBench creates a new evaluation benchmark for low-level embodied spatial intelligence in VLMs, evaluates 13 models showing gaps in interaction perception, and supplies a large synthetic training set that yields measurable gains.
HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
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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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cs.CV 1years
2026 1verdicts
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Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models
Embodied3DBench creates a new evaluation benchmark for low-level embodied spatial intelligence in VLMs, evaluates 13 models showing gaps in interaction perception, and supplies a large synthetic training set that yields measurable gains.