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.
Gualtieri et al. REF search proper grasp pose in clutter through generating a large set of candidates and classifying them into "good" or "bad".
High precision grasp pose detection in dense clutter
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Computer Science Engineering