Diffusion Suction Grasping with Large-Scale Parcel Dataset

Ding-Tao Huang, Debei Hua, Dongfang Yu, Xinyi He, En-Te Lin, Liang-Hong Wang, Jin-Liang Hou, Long Zeng

Abstract: While recent advances in suction grasping have shown remarkable progress, significant challenges persist particularly in cluttered and complex parcel handling scenarios. Current approaches are limited by (1) the lack of comprehensive parcel-specific suction grasp datasets and (2) poor adaptability to diverse object properties, including size, geometry, and texture. We address these challenges through two main contributions. Firstly, we introduce the Parcel-Suction-Dataset, a large-scale synthetic dataset containing 25 thousand cluttered scenes with 410 million precision-annotated suction grasp poses, generated via our novel geometric sampling algorithm. Secondly, we propose Diffusion-Suction, a framework that innovatively reformulates suction grasp prediction as a conditional generation task using denoising diffusion probabilistic models. Our method iteratively refines random noise into suction grasping score through visual-conditioned guidance from point cloud observations, effectively learning spatial point-wise affordances from our synthetic dataset. Extensive experiments demonstrate that the simple yet efficient Diffusion-Suction achieves new state-of-the-art performance compared to previous models on both Parcel-Suction-Dataset and the public SuctionNet-1Billion benchmark. This work provides a robust foundation for advancing automated parcel handling systems in real-world applications.

External IDs:dblp:conf/iros/HuangHYHLWHZ25