  We present an accurate, real-time approach to robotic grasp detection based
on convolutional neural networks. Our network performs single-stage regression
to graspable bounding boxes without using standard sliding window or region
proposal techniques. The model outperforms state-of-the-art approaches by 14
percentage points and runs at 13 frames per second on a GPU. Our network can
simultaneously perform classification so that in a single step it recognizes
the object and finds a good grasp rectangle. A modification to this model
predicts multiple grasps per object by using a locally constrained prediction
mechanism. The locally constrained model performs significantly better,
especially on objects that can be grasped in a variety of ways.
