  State-of-the-art deep neural network recognition systems are designed for a
static and closed world. It is usually assumed that the distribution at test
time will be the same as the distribution during training. As a result,
classifiers are forced to categorise observations into one out of a set of
predefined semantic classes. Robotic problems are dynamic and open world; a
robot will likely observe objects that are from outside of the training set
distribution. Classifier outputs in robotic applications can lead to real-world
robotic action and as such, a practical recognition system should not silently
fail by confidently misclassifying novel observations. We show how a deep
metric learning classification system can be applied to such open set
recognition problems, allowing the classifier to label novel observations as
unknown. Further to detecting novel examples, we propose an open set active
learning approach that allows a robot to efficiently query a user about unknown
observations. Our approach enables a robot to improve its understanding of the
true distribution of data in the environment, from a small number of label
queries. Experimental results show that our approach significantly outperforms
comparable methods in both the open set recognition and active learning
problems.
