  Data-efficient domain adaptation with only a few labelled data is desired for
many robotic applications, e.g., in grasping detection, the inference skill
learned from a grasping dataset is not universal enough to directly apply on
various other daily/industrial applications. This paper presents an approach
enabling the easy domain adaptation through a novel grasping detection network
with confidence-driven semi-supervised learning, where these two components
deeply interact with each other. The proposed grasping detection network
specially provides a prediction uncertainty estimation mechanism by leveraging
on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning
utilizes such uncertainty information to emphasizing the consistency loss only
for those unlabelled data with high confidence, which we referred it as the
confidence-driven mean teacher. This approach largely prevents the student
model to learn the incorrect/harmful information from the consistency loss,
which speeds up the learning progress and improves the model accuracy. Our
results show that the proposed network can achieve high success rate on the
Cornell grasping dataset, and for domain adaptation with very limited data, the
confidence-driven mean teacher outperforms the original mean teacher and direct
training by more than 10% in evaluation loss especially for avoiding the
overfitting and model diverging.
