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Grasping for novel objects is important for robot manipulation inunstructured environments. Most of current works require a grasp samplingprocess to obtain grasp candidates, combined with local feature extractor usingdeep learning. This pipeline is time-costly, expecially when grasp points aresparse such as at the edge of a bowl. In this paper, we propose an end-to-endapproach to directly predict the poses, categories and scores (qualities) ofall the grasps. It takes the whole sparse point clouds as the input andrequires no sampling or search process. Moreover, to generate training data ofmulti-object scene, we propose a fast multi-object grasp detection algorithmbased on Ferrari Canny metrics. A single-object dataset (79 objects from YCBobject set, 23.7k grasps) and a multi-object dataset (20k point clouds withannotations and masks) are generated. A PointNet++ based network combined withmulti-mask loss is introduced to deal with different training points. The wholeweight size of our network is only about 11.6M, which takes about 102ms for awhole prediction process using a GeForce 840M GPU. Our experiment shows ourwork get 71.43% success rate and 91.60% completion rate, which performs betterthan current state-of-art works.

PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds