  This work considers robot keypoint estimation on color images as a supervised
machine learning task. We propose the use of probabilistically created
renderings to overcome the lack of labeled real images. Rather than sampling
from stationary distributions, our approach introduces a feedback mechanism
that constantly adapts probability distributions according to current training
progress. Initial results show, our approach achieves near-human-level accuracy
on real images. Additionally, we demonstrate that feedback leads to fewer
required training steps, while maintaining the same model quality on synthetic
data sets.
