Planar grasp detection using generative multi-column convolutional neural networks

Abstract

This paper presents an accurate, real-time generative multi-column convolutional neural network for two-dimension planner grasp detection. A multi-column structure is used to improve the ability of the network to extract features of different scales. Three parallel channels with three different reception fields can make the network learn different scales of features, which result in the network is more adaptable to a complex environment. This network overcomes some shortcomings like long computing period and by using a generative method rather than sampling grasping candidate. Our network has short computing time because of its light structure, which can be deployed in some close-loop situations. While training the network, we find that some labels in Cornell database is not suitable for the planner grasping detection training, for some specific labels represent different angel of grasping. By comparison with other models, our models accuracy on Cornell grasping dataset is higher, reaching 94%, and our model runs at 13 frames per second.