  Despite the impressive progress achieved in robotic grasping, robots are not
skilled in sophisticated tasks (e.g. search and grasp a specified target in
clutter). Such tasks involve not only grasping but the comprehensive perception
of the world (e.g. the object relationships). Recently, encouraging results
demonstrate that it is possible to understand high-level concepts by learning.
However, such algorithms are usually data-intensive, and the lack of data
severely limits their performance. In this paper, we present a new dataset
named REGRAD for the learning of relationships among objects and grasps. We
collect the annotations of object poses, segmentations, grasps, and
relationships for the target-driven relational grasping tasks. Our dataset is
collected in both forms of 2D images and 3D point clouds. Moreover, since all
the data are generated automatically, it is free to import new objects for data
generation. We also released a real-world validation dataset to evaluate the
sim-to-real performance of models trained on REGRAD. Finally, we conducted a
series of experiments, showing that the models trained on REGRAD could
generalize well to the realistic scenarios, in terms of both relationship and
grasp detection. Our dataset and code could be found at:
https://github.com/poisonwine/REGRAD
