Elizabeth Carter

As robots are deployed to work in our environments, we must build appropriate expectations of their behavior so that we can trust them to perform their jobs autonomously as we attend to other tasks. Many types of explanations for robot behavior have been proposed, but they have not been fully analyzed for their impact on aligning expectations of robot paths for navigation. In this work, we evaluate several types of robot navigation explanations to understand their impact on the ability of humans to anticipate a robot’s paths. We performed an experiment in which we gave participants an explanation of a robot path and then measured (i) their ability to predict that path, (ii) their allocation of attention on the robot navigating the path versus their own dot-tracking task, and (iii) their subjective ratings of the robot’s predictability and trustworthiness. Our results show that explanations do significantly affect people’s ability to predict robot paths and that explanations that are concise and do not require readers to perform mental transformations are most effective at reducing attention to the robot.