Towards Transparent Robotic Planning via Contrastive Explanations
This addresses the need for improved transparency and trust in robotic systems for users, though it is incremental as it builds on existing social science insights and planning frameworks.
The paper tackled the problem of making robotic planning more transparent by generating contrastive explanations, which explain why one action is chosen over another, and found that these explanations increased user understanding and trust while reducing cognitive burden in a study with 100 participants.
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.