How robots' questions affect the accuracy of the human responses

Asking questions is an inevitable part of collaborative interactions between humans and robots. However, robotics novices may have difficulty answering the robots' questions if they do not understand what the robot is asking. We are particularly interested in whether robots can supplement their questions with information about their state in a manner that increases the accuracy of human responses. In this work, we design and carefully analyze a human-robot collaborative task experiment to measure humans' responses and accuracies to different amounts of supplemental information. We vary the content of the questions along four dimensions of the robot state, namely uncertainty, context, predictions, and feature selection. Based on our results, we contribute guidelines on the effective combination of the four dimensions, under the assumption that the robot has no limitations on generating question context. Finally, we validate our guidelines against educated recommendations from the HRI community.