H^{3}DP: Triply-Hierarchical Diffusion Policy for Visuomotor
  Learning
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/.
6822e15ef544b9a0196b855d
Yiyang Lu
claimed_verified
2025-05-13T08:11:25.750Z
661678b244425d16e37f2341
https://cdn-avatars.huggingface.co/v1/production/uploads/661678b244425d16e37f2341/VUSQ3S0IXZLoIxO4oX4cv.png
Yiyang Lu
user
Lyy0725
6822e15ef544b9a0196b855e
Yufeng Tian
6822e15ef544b9a0196b855f
Zhecheng Yuan
6822e15ef544b9a0196b8560
Xianbang Wang
claimed_verified
2025-05-13T08:40:08.476Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/LM9pPOgHXg2I7nn2A632t.png
Xianbang Wang
user
binaryXwizard
6822e15ef544b9a0196b8561
Pu Hua
6822e15ef544b9a0196b8562
Zhengrong Xue
6822e15ef544b9a0196b8563
Huazhe Xu
H^{3}DP: Triply-Hierarchical Diffusion Policy for Visuomotor
  Learning
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/.
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.
Triply-Hierarchical Diffusion Policy H$^{3}$DP depth-aware input layering multi-scale visual representations hierarchically conditioned diffusion process visuomotor policy learning generative models