Diffusion Suction Grasping with Large-Scale Parcel Dataset

TL;DR

This paper introduces a large-scale synthetic dataset for parcel suction grasping and proposes a novel diffusion-based framework that significantly improves grasp prediction accuracy in cluttered environments.

Contribution

The work provides the first comprehensive parcel suction dataset and a diffusion probabilistic model for grasp prediction, advancing the state-of-the-art in complex parcel handling.

Findings

Achieved state-of-the-art performance on Parcel-Suction-Dataset

Outperformed previous models on SuctionNet-1Billion benchmark

Demonstrated effective learning of spatial affordances from synthetic data

Abstract

While recent advances in object suction grasping have shown remarkable progress, significant challenges persist particularly in cluttered and complex parcel handling scenarios. Two fundamental limitations hinder current approaches: (1) the lack of a comprehensive suction grasp dataset tailored for parcel manipulation tasks, and (2) insufficient adaptability to diverse object characteristics including size variations, geometric complexity, and textural diversity. To address these challenges, we present Parcel-Suction-Dataset, a large-scale synthetic dataset containing 25 thousand cluttered scenes with 410 million precision-annotated suction grasp poses. This dataset is generated through our novel geometric sampling algorithm that enables efficient generation of optimal suction grasps incorporating both physical constraints and material properties. We further propose Diffusion-Suction, an innovative framework that reformulates suction grasp prediction as a conditional generation task through denoising diffusion probabilistic models. Our method iteratively refines random noise into suction grasp score maps 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.