H$^3$DP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning (2505.07819v2)

Abstract: 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 $\textbf{Triply-Hierarchical Diffusion Policy}~(\textbf{H$^{\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 $\mathbf{3}$ 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 $\mathbf{+27.5\%}$ average relative improvement over baselines across $\mathbf{44}$ simulation tasks and achieves superior performance in $\mathbf{4}$ challenging bimanual real-world manipulation tasks. Project Page: https://lyy-iiis.github.io/h3dp/.

• The paper introduces H3DP, a triply-hierarchical diffusion policy framework achieving significant average performance gains of +27.5% in simulations and +32.3% on real-world robotic manipulation tasks compared to baselines.
• H3DP employs a unique three-layer hierarchy including depth-aware input layering, multi-scale visual representation, and a hierarchically conditioned diffusion process to tightly integrate perception and action.
• This hierarchical approach improves robustness and adaptability for complex tasks like manipulating articulated objects and aligns robotic learning with cognitive models of human decision-making.

Overview of H$^{\mathbf{3}}$DP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning

Introduction

The paper introduces the Triply-Hierarchical Diffusion Policy (H$^{3}$DP), a novel framework in visuomotor learning designed to address challenges in robotic manipulation. The authors critique existing visuomotor learning approaches, which often fail to tightly couple visual perception with action generation. Inspired by hierarchical processing in human decision-making, H$^{3}$DP integrates three distinct hierarchical structures to enhance learning efficacy.

Methodological Advancements

Hierarchical Structures

H$^{3}$DP implements a triply-hierarchical structure that encompasses:

1. Depth-Aware Input Layering: Utilizing RGB-D images, the framework organizes input data into depth-sensitive layers. This arrangement promotes better discrimination between foreground and background elements, enhancing spatial awareness.
2. Multi-Scale Visual Representation: This component captures visual features at different granularities, ranging from global to fine details. This multi-scale approach retains semantic information across varying levels of abstraction.
3. Hierarchically Conditioned Diffusion Process: The policy employs a hierarchical diffusion model, progressively transforming actions from coarse to fine resolutions. Initial denoising uses coarse features to define global structures, while finer features guide detailed refinements.

Numerical Results and Claims

The paper presents strong empirical evidence for the efficacy of H$^{3}$DP. Key results include:

• A +27.5% average relative improvement across 44 simulation tasks compared to existing baselines, demonstrating superior performance in simulated environments.
• Application to four challenging real-world tasks showing a +32.3% performance enhancement over existing diffusion policy methods.

Implications and Future Prospects

Practical Implications

The triply-hierarchical design facilitates robust visuomotor learning, potentially improving how autonomous systems perform in complex environments. By integrating depth-aware layering and multi-scale visual input, H$^{3}$DP provides a framework that is highly adaptive to various manipulation challenges, such as those involving articulated or deformable objects.

Theoretical Implications

The hierarchical approach reinforces the correlation between perception and action, aligning the learning framework with cognitive models of human decision-making. This pivot could inspire further research into hierarchical modeling techniques in AI and robotics.

Speculative Future Developments

Looking ahead, this research offers promising paths for advancing AI's capabilities in navigation, interaction, and manipulation. Further exploration might focus on refining hierarchical processes within other domains of AI, including autonomous driving or UAV control systems. The paper also opens up avenues for inquiry into optimizing diffusion models for real-time applications, especially in scenarios requiring rapid inferential adjustments.

Conclusion

H$^{3}$DP represents a significant step in integrating hierarchical constructs into visuomotor learning, offering substantial improvements in robotic manipulation tasks. The paper’s methodological contributions promise to enhance future research in AI's practical applications and theoretical frameworks. As AI systems continue to evolve, embracing complexity through hierarchical approaches akin to those illustrated by H$^{3}$DP may be crucial to achieving higher levels of autonomy and generalization.