GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy (2410.17488v1)

Abstract: Diffusion-based policies have shown remarkable capability in executing complex robotic manipulation tasks but lack explicit characterization of geometry and semantics, which often limits their ability to generalize to unseen objects and layouts. To enhance the generalization capabilities of Diffusion Policy, we introduce a novel framework that incorporates explicit spatial and semantic information via 3D semantic fields. We generate 3D descriptor fields from multi-view RGBD observations with large foundational vision models, then compare these descriptor fields against reference descriptors to obtain semantic fields. The proposed method explicitly considers geometry and semantics, enabling strong generalization capabilities in tasks requiring category-level generalization, resolving geometric ambiguities, and attention to subtle geometric details. We evaluate our method across eight tasks involving articulated objects and instances with varying shapes and textures from multiple object categories. Our method demonstrates its effectiveness by increasing Diffusion Policy's average success rate on unseen instances from 20% to 93%. Additionally, we provide a detailed analysis and visualization to interpret the sources of performance gain and explain how our method can generalize to novel instances.

• The paper presents a novel framework that integrates 3D spatial and semantic cues to improve diffusion policies in robotic manipulation.
• It utilizes multi-view RGBD observations to create 3D descriptor fields and semantic fields, resolving geometric ambiguities in task execution.
• The approach boosts success rates on unseen instances from 20% to 93%, demonstrating significant improvements in category-level generalization.

Overview of GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy

The paper "GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy" introduces a novel framework designed to enhance the generalization capabilities of diffusion-based policies in robotic manipulation tasks. This approach specifically addresses the limitations associated with traditional diffusion policies, which often struggle to generalize to unseen objects and layouts due to their lack of explicit geometry and semantic characterization.

Contribution and Methodology

The research presents an innovative framework that leverages 3D semantic fields to incorporate explicit spatial and semantic information. This is achieved through the generation of 3D descriptor fields from multi-view RGBD observations, utilizing large foundational vision models like DINOv2. The framework then compares these descriptor fields with reference descriptors to construct semantic fields. This explicit integration of geometry and semantics facilitates the resolution of geometric ambiguities and enhances attention to subtle geometric details.

The core components of the proposed framework include:

• 3D Descriptor Fields Encoder: Deriving high-dimensional descriptors from multi-view observations to represent the environment’s geometry.
• Semantic Fields Constructor: Transforming high-dimensional descriptors into low-dimensional semantic fields, highlighting semantically meaningful parts.
• Action Policy: Utilizing the semantic fields and point cloud to predict actions through a diffusion model.

Experimental Results

The experimental setup comprises eight tasks involving articulated objects across diverse categories and examines the framework's capacity for category-level generalization. The authors report a notable increase in the diffusion policy's average success rate on unseen instances from 20\% to an impressive 93%. This substantial improvement underscores the impact of incorporating geometric and semantic cues in enhancing the generalization capacity of diffusion policies.

The results highlight the framework's effectiveness in scenarios involving:

• Category-Level Generalization: The ability to generalize across instances within a category by focusing on semantically meaningful parts vital for task completion.
• Geometric Ambiguity Resolution: Differentiating between geometrically similar but functionally distinct object parts, such as the knife blade and handle.
• Attention to Subtle Semantic Details: Recognizing and attending to nuanced details essential for task success, even amidst observational noise.

Implications and Speculations

The proposed GenDP framework presents significant practical and theoretical implications. Practically, this approach demonstrates promising improvements in robotic manipulation tasks, particularly in the context of unseen object instances and varying environments. Theoretically, it advances the understanding of integrating semantics and spatial information into diffusion models, thereby contributing to developments in imitation learning and semantic perception.

For future directions, researchers could explore the potential for adaptive task-specific semantic fields that evolve in real-time to account for the progression of complex tasks. Additionally, integrating explicit geometric properties could enhance interpretability and efficiency, offering new avenues for further research in robotics.

Conclusion

The paper offers a compelling approach to overcoming the limitations of traditional diffusion policies by integrating 3D spatial and semantic data. The noticeable increase in success rates when handling novel objects underscores the framework's robustness and potential applicability to a wide array of robotic tasks, paving the way for more adaptable and efficient robotic systems in real-world scenarios.