  Predicting human intention is critical to facilitating safe and efficient
human-robot collaboration (HRC). However, it is challenging to build
data-driven models for human intention prediction. One major challenge is due
to the diversity and noise in human motion data. It is expensive to collect a
massive motion dataset that comprehensively covers all possible scenarios,
which leads to the scarcity of human motion data in certain scenarios, and
therefore, causes difficulties in constructing robust and reliable intention
predictors. To address the challenge, this paper proposes an iterative
adversarial data augmentation (IADA) framework to learn neural network models
from an insufficient amount of training data. The method uses neural network
verification to identify the most "confusing" input samples and leverages
expert guidance to safely and iteratively augment the training data with these
samples. The proposed framework is applied to collected human datasets. The
experiments demonstrate that our method can achieve more robust and accurate
prediction performance compared to existing training methods.
