Robot nonverbal behavior improves task performance in difficult collaborations

Abstract

Nonverbal behaviors increase task efficiency and improve collaboration between people and robots. In this paper, we introduce a model for generating nonverbal behavior and investigate whether the usefulness of nonverbal behaviors changes based on task difficulty. First, we detail a robot behavior model that accounts for top-down and bottom-up features of the scene when deciding when and how to perform deictic references (looking or pointing). Then, we analyze how a robot's deictic nonverbal behavior affects people's performance on a memorization task under differing difficulty levels. We manipulate difficulty in two ways: by adding steps to memorize, and by introducing an interruption. We find that when the task is easy, the robot's nonverbal behavior has little influence over recall and task completion. However, when the task is challenging- because the memorization load is high or because the task is interrupted-a robot's nonverbal behaviors mitigate the negative effects of these challenges, leading to higher recall accuracy and lower completion times. In short, nonverbal behavior may be even more valuable for difficult collaborations than for easy ones.