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.

本研究旨在利用深度传感器扫描的大型在线数据集来训练卷积神经网络，通过两种新的抓取候选表示方法实现对 6 自由度抓取候选的分类来提高抓取方法的成功率，在 Baxter 研究机器人上的实验中，相对于以前的研究成果，我们的方法有了 20％的提高，平均抓取成功率达到了 93％。