  Real-world robotics applications demand object pose estimation methods that
work reliably across a variety of scenarios. Modern learning-based approaches
require large labeled datasets and tend to perform poorly outside the training
domain. Our first contribution is to develop a robust corrector module that
corrects pose estimates using depth information, thus enabling existing methods
to better generalize to new test domains; the corrector operates on semantic
keypoints (but is also applicable to other pose estimators) and is fully
differentiable. Our second contribution is an ensemble self-training approach
that simultaneously trains multiple pose estimators in a self-supervised
manner. Our ensemble self-training architecture uses the robust corrector to
refine the output of each pose estimator; then, it evaluates the quality of the
outputs using observable correctness certificates; finally, it uses the
observably correct outputs for further training, without requiring external
supervision. As an additional contribution, we propose small improvements to a
regression-based keypoint detection architecture, to enhance its robustness to
outliers; these improvements include a robust pooling scheme and a robust
centroid computation. Experiments on the YCBV and TLESS datasets show the
proposed ensemble self-training outperforms fully supervised baselines while
not requiring 3D annotations on real data.
