  Providing explanations of chosen robotic actions can help to increase the
transparency of robotic planning and improve users' trust. Social sciences
suggest that the best explanations are contrastive, explaining not just why one
action is taken, but why one action is taken instead of another. We formalize
the notion of contrastive explanations for robotic planning policies based on
Markov decision processes, drawing on insights from the social sciences. We
present methods for the automated generation of contrastive explanations with
three key factors: selectiveness, constrictiveness, and responsibility. The
results of a user study with 100 participants on the Amazon Mechanical Turk
platform show that our generated contrastive explanations can help to increase
users' understanding and trust of robotic planning policies while reducing
users' cognitive burden.
