RoI-based Robotic Grasp Detection in Object Overlapping Scenes Using Convolutional Neural Network

Grasp detection is an essential skill for widespread use of robots. Recent works demonstrate the advanced performance of Convolutional Neural Network (CNN) on robotic grasp detection. However, a significant shortcoming of existing grasp detection algorithms is that they all ignore the affiliation between grasps and targets. In this paper, we propose a robotic grasp detection algorithm based on Region of Interest (RoI) to simultaneously detect targets and their grasps in object overlapping scenes. Our proposed algorithm uses Regions of Interest (RoIs) to detect grasps while doing classification and location regression of targets. To train the network, we contribute a much bigger multi-object grasp dataset than Cornell Grasp Dataset, which is based on Visual Manipulation Relationship Dataset. Experimental results demonstrate that our algorithm achieves 24.0% miss rate at 1FPPI and 70.5% mAP with grasp on our dataset. Robotic experiments demonstrate that our proposed algorithm can help robots grasp specified target in multi-object scenes at 84% success rate.