Research on Interactive Robot Learning has yielded several modalities for querying a human for training data, including demonstrations, preferences, and corrections. While prior work in this space has focused on optimizing the robot’s queries within each interaction type, there has been little work on optimizing over the selection of the interaction type itself. We present INQUIRE, the first algorithm to implement and optimize over a generalized representation of information gain across multiple interaction types. Our evaluations show that INQUIRE can
dynamically optimize its interaction type (and respective optimal query) based on its current learning status and the robot’s state in the world, resulting in more robust performance across tasks in comparison to state-of-the-art baseline methods. Additionally, INQUIRE allows for customizable cost metrics to bias its selection of interaction types, enabling this algorithm to be tailored to a robot’s particular deployment domain and formulate cost-aware, informative queries.