  Despite the impressive progress achieved in robust grasp detection, robots
are not skilled in sophisticated grasping tasks (e.g. search and grasp a
specific object in clutter). Such tasks involve not only grasping, but
comprehensive perception of the visual world (e.g. the relationship between
objects). Recently, the advanced deep learning techniques provide a promising
way for understanding the high-level visual concepts. It encourages robotic
researchers to explore solutions for such hard and complicated fields. However,
deep learning usually means data-hungry. The lack of data severely limits the
performance of deep-learning-based algorithms. In this paper, we present a new
dataset named \regrad to sustain the modeling of relationships among objects
and grasps. We collect the annotations of object poses, segmentations, grasps,
and relationships in each image for comprehensive perception of grasping. Our
dataset is collected in both forms of 2D images and 3D point clouds. Moreover,
since all the data are generated automatically, users are free to import their
own object models for the generation of as many data as they want. We have
released our dataset and codes. A video that demonstrates the process of data
generation is also available.
