title: H^{3}DP: Triply-Hierarchical Diffusion Policy for Visuomotor
    Learning
summary: Visuomotor policy learning has witnessed substantial progress in robotic
  manipulation, with recent approaches predominantly relying on generative models
  to model the action distribution. However, these methods often overlook the
  critical coupling between visual perception and action prediction. In this
  work, we introduce Triply-Hierarchical Diffusion
  Policy~(H^{\mathbf{3}DP}), a novel visuomotor learning framework
  that explicitly incorporates hierarchical structures to strengthen the
  integration between visual features and action generation. H^{3}DP contains
  3 levels of hierarchy: (1) depth-aware input layering that organizes
  RGB-D observations based on depth information; (2) multi-scale visual
  representations that encode semantic features at varying levels of granularity;
  and (3) a hierarchically conditioned diffusion process that aligns the
  generation of coarse-to-fine actions with corresponding visual features.
  Extensive experiments demonstrate that H^{3}DP yields a +27.5%
  average relative improvement over baselines across 44 simulation
  tasks and achieves superior performance in 4 challenging bimanual
  real-world manipulation tasks. Project Page: https://lyy-iiis.github.io/h3dp/.
paper_authors._id: 6822e15ef544b9a0196b855d
paper_authors.name: Yiyang Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T08:11:25.750Z
paper_authors._id: 661678b244425d16e37f2341
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/661678b244425d16e37f2341/VUSQ3S0IXZLoIxO4oX4cv.png
paper_authors.fullname: Yiyang Lu
paper_authors.type: user
paper_authors.user: Lyy0725
paper_authors._id: 6822e15ef544b9a0196b855e
paper_authors.name: Yufeng Tian
paper_authors._id: 6822e15ef544b9a0196b855f
paper_authors.name: Zhecheng Yuan
paper_authors._id: 6822e15ef544b9a0196b8560
paper_authors.name: Xianbang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T08:40:08.476Z
paper_authors._id: 6738670f5deec72e829e99e0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/LM9pPOgHXg2I7nn2A632t.png
paper_authors.fullname: Xianbang Wang
paper_authors.type: user
paper_authors.user: binaryXwizard
paper_authors._id: 6822e15ef544b9a0196b8561
paper_authors.name: Pu Hua
paper_authors._id: 6822e15ef544b9a0196b8562
paper_authors.name: Zhengrong Xue
paper_authors._id: 6822e15ef544b9a0196b8563
paper_authors.name: Huazhe Xu
paper_title: H^{3}DP: Triply-Hierarchical Diffusion Policy for Visuomotor
    Learning
paper_summary: Visuomotor policy learning has witnessed substantial progress in robotic
  manipulation, with recent approaches predominantly relying on generative models
  to model the action distribution. However, these methods often overlook the
  critical coupling between visual perception and action prediction. In this
  work, we introduce Triply-Hierarchical Diffusion
  Policy~(H^{\mathbf{3}DP}), a novel visuomotor learning framework
  that explicitly incorporates hierarchical structures to strengthen the
  integration between visual features and action generation. H^{3}DP contains
  3 levels of hierarchy: (1) depth-aware input layering that organizes
  RGB-D observations based on depth information; (2) multi-scale visual
  representations that encode semantic features at varying levels of granularity;
  and (3) a hierarchically conditioned diffusion process that aligns the
  generation of coarse-to-fine actions with corresponding visual features.
  Extensive experiments demonstrate that H^{3}DP yields a +27.5%
  average relative improvement over baselines across 44 simulation
  tasks and achieves superior performance in 4 challenging bimanual
  real-world manipulation tasks. Project Page: https://lyy-iiis.github.io/h3dp/.
paper_ai_summary: H$^{3}$DP, a triply-hierarchical diffusion policy, integrates visual perception and action prediction through depth-aware layers, multi-scale representations, and hierarchically conditioned diffusion, improving visuomotor performance over baselines.
paper_ai_keywords: Triply-Hierarchical Diffusion Policy H$^{3}$DP depth-aware input layering multi-scale visual representations hierarchically conditioned diffusion process visuomotor policy learning generative models