ROI-based Robotic Grasp Detection for Object Overlapping Scenes

TL;DR

This paper introduces ROI-GD, a region of interest-based grasp detection algorithm that improves robotic grasping in overlapping object scenes, demonstrating high success rates in real-world experiments.

Contribution

The paper proposes ROI-GD, a novel two-stage grasp detection method utilizing ROI features, and provides a large multi-object grasp dataset for improved training and evaluation.

Findings

ROI-GD outperforms existing methods in overlapping scenes.

Achieves 92.5% success in single-object grasping.

Achieves 83.8% success in multi-object grasping.

Abstract

Grasp detection with consideration of the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector based on ROI features. We also contribute a multi-object grasp dataset, which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can help robots grasp the target in single-object and multi-object scenes with the overall success rates of 92.5% and 83.8% respectively.