  In this paper, we present a real-time approach to predict multiple grasping
poses for a parallel-plate robotic gripper using RGB images. A model with
oriented anchor box mechanism is proposed and a new matching strategy is used
during the training process. An end-to-end fully convolutional neural network
is employed in our work. The network consists of two parts: the feature
extractor and multi-grasp predictor. The feature extractor is a deep
convolutional neural network. The multi-grasp predictor regresses grasp
rectangles from predefined oriented rectangles, called oriented anchor boxes,
and classifies the rectangles into graspable and ungraspable. On the standard
Cornell Grasp Dataset, our model achieves an accuracy of 97.74% and 96.61% on
image-wise split and object-wise split respectively, and outperforms the latest
state-of-the-art approach by 1.74% on image-wise split and 0.51% on object-wise
split.

Robotics;Computer Vision and Pattern Recognition