  The ability to segment unknown objects in cluttered scenes has a profound
impact on robot grasping. The rise of deep learning has greatly transformed the
pipeline of robotic grasping from model-based approach to data-driven stream,
which generally requires a large scale of grasping data either collected in
simulation or from real-world examples. In this paper, we proposed a
sim-to-real framework to transfer the object segmentation model learned in
simulation to the real-world. First, data samples are collected in simulation,
including RGB, 6D pose, and point cloud. Second, we also present a GAN-based
unknown object segmentation method through domain adaptation, which consists of
an image translation module and an image segmentation module. The image
translation module is used to shorten the reality gap and the segmentation
module is responsible for the segmentation mask generation. We used the above
method to perform segmentation experiments on unknown objects in a bin-picking
scenario. Finally, the experimental result shows that the segmentation model
learned in simulation can be used for real-world data segmentation.
