  Robots learning a new manipulation task from a small amount of demonstrations
are increasingly demanded in different workspaces. A classifier model assessing
the quality of actions can predict the successful completion of a task, which
can be used by intelligent agents for action-selection. This paper presents a
novel classifier that learns to classify task completion only from a few
demonstrations. We carry out a comprehensive comparison of different neural
classifiers, e.g. fully connected-based, fully convolutional-based,
sequence2sequence-based, and domain adaptation-based classification. We also
present a new dataset including five robot manipulation tasks, which is
publicly available. We compared the performances of our novel classifier and
the existing models using our dataset and the MIME dataset. The results suggest
domain adaptation and timing-based features improve success prediction. Our
novel model, i.e. fully convolutional neural network with domain adaptation and
timing features, achieves an average classification accuracy of 97.3\% and
95.5\% across tasks in both datasets whereas state-of-the-art classifiers
without domain adaptation and timing-features only achieve 82.4\% and 90.3\%,
respectively.
