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

TL;DR

This paper introduces GenDP, a framework that enhances diffusion-based robotic policies by incorporating 3D semantic fields derived from multi-view RGBD data, significantly improving generalization to unseen objects and layouts.

Contribution

The paper presents a novel method that explicitly encodes geometry and semantics into diffusion policies using 3D descriptor fields, enabling better generalization across object categories.

Findings

Success rate on unseen instances increased from 20% to 93%.

Method effectively resolves geometric ambiguities and captures subtle details.

Demonstrates strong generalization across multiple object categories.

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.