  This paper considers the problem of grasp pose detection in point clouds. We
follow a general algorithmic structure that first generates a large set of
6-DOF grasp candidates and then classifies each of them as a good or a bad
grasp. Our focus in this paper is on improving the second step by using depth
sensor scans from large online datasets to train a convolutional neural
network. We propose two new representations of grasp candidates, and we
quantify the effect of using prior knowledge of two forms: instance or category
knowledge of the object to be grasped, and pretraining the network on simulated
depth data obtained from idealized CAD models. Our analysis shows that a more
informative grasp candidate representation as well as pretraining and prior
knowledge significantly improve grasp detection. We evaluate our approach on a
Baxter Research Robot and demonstrate an average grasp success rate of 93% in
dense clutter. This is a 20% improvement compared to our prior work.
