  Robustness in deep neural networks and machine learning algorithms in general
is an open research challenge. In particular, it is difficult to ensure
algorithmic performance is maintained on out-of-distribution inputs or
anomalous instances that cannot be anticipated at training time. Embodied
agents will be deployed in these conditions, and are likely to make incorrect
predictions. An agent will be viewed as untrustworthy unless it can maintain
its performance in dynamic environments. Most robust training techniques aim to
improve model accuracy on perturbed inputs; as an alternate form of robustness,
we aim to reduce the severity of mistakes made by neural networks in
challenging conditions. We leverage current adversarial training methods to
generate targeted adversarial attacks during the training process in order to
increase the semantic similarity between a model's predictions and true labels
of misclassified instances. Results demonstrate that our approach performs
better with respect to mistake severity compared to standard and adversarially
trained models. We also find an intriguing role that non-robust features play
with regards to semantic similarity.
