HeRO: Hierarchical 3D Semantic Representation for Pose-aware Object Manipulation

• URL: http://arxiv.org/abs/2602.18817v1
• Date: Sat, 21 Feb 2026 12:29:10 GMT
• Title: HeRO: Hierarchical 3D Semantic Representation for Pose-aware Object Manipulation
• Authors: Chongyang Xu, Shen Cheng, Haipeng Li, Haoqiang Fan, Ziliang Feng, Shuaicheng Liu,
• Abstract summary: HeRO is a diffusion-based policy that couples geometry and semantics via hierarchical semantic fields.<n>In various tests, HeRO establishes a new state-of-the-art, improving success on Place Dual Shoes by 12.3% and averaging 6.5% gains across six challenging pose-aware tasks.
• Score: 54.325346533275074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imitation learning for robotic manipulation has progressed from 2D image policies to 3D representations that explicitly encode geometry. Yet purely geometric policies often lack explicit part-level semantics, which are critical for pose-aware manipulation (e.g., distinguishing a shoe's toe from heel). In this paper, we present HeRO, a diffusion-based policy that couples geometry and semantics via hierarchical semantic fields. HeRO employs dense semantics lifting to fuse discriminative, geometry-sensitive features from DINOv2 with the smooth, globally coherent correspondences from Stable Diffusion, yielding dense features that are both fine-grained and spatially consistent. These features are processed and partitioned to construct a global field and a set of local fields. A hierarchical conditioning module conditions the generative denoiser on global and local fields using permutation-invariant network architecture, thereby avoiding order-sensitive bias and producing a coherent control policy for pose-aware manipulation. In various tests, HeRO establishes a new state-of-the-art, improving success on Place Dual Shoes by 12.3% and averaging 6.5% gains across six challenging pose-aware tasks. Code is available at https://github.com/Chongyang-99/HeRO.

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