  The rise of deep learning has greatly transformed the pipeline of robotic
grasping from model-based approach to data-driven stream. Along this line, a
large scale of grasping data either collected from simulation or from real
world examples become extremely important. In this paper, we present our recent
work on data generation in simulation for a bin-picking scene. 77 objects from
the YCB object data sets are used to generate the dataset with PyBullet, where
different environment conditions are taken into account including lighting,
camera pose, sensor noise and so on. In all, 100K data samples are collected in
terms of ground truth segmentation, RGB, 6D pose and point cloud. All the data
examples including the source code are made available online.
