  In this paper, we propose an iterative self-training framework for
sim-to-real 6D object pose estimation to facilitate cost-effective robotic
grasping. Given a bin-picking scenario, we establish a photo-realistic
simulator to synthesize abundant virtual data, and use this to train an initial
pose estimation network. This network then takes the role of a teacher model,
which generates pose predictions for unlabeled real data. With these
predictions, we further design a comprehensive adaptive selection scheme to
distinguish reliable results, and leverage them as pseudo labels to update a
student model for pose estimation on real data. To continuously improve the
quality of pseudo labels, we iterate the above steps by taking the trained
student model as a new teacher and re-label real data using the refined teacher
model. We evaluate our method on a public benchmark and our newly-released
dataset, achieving an ADD(-S) improvement of 11.49% and 22.62% respectively.
Our method is also able to improve robotic bin-picking success by 19.54%,
demonstrating the potential of iterative sim-to-real solutions for robotic
applications.
