Learning to Interact with a Human Partner

Mayada Oudah, Vahan Babushkin, Tennom Chenlinangjia, Jacob W. Crandall

Abstract: Despite the importance of mutual adaption in human relationships, online learning is not yet used during most successful human-robot interactions. The lack of online learning in HRI to date can be attributed to at least two unsolved challenges: random exploration (a core component of most online-learning algorithms) and the slow convergence rates of previous online-learning algorithms. However, several recently developed online-learning algorithms have been reported to learn at much faster rates than before, which makes them candidates for use in human-robot interactions. In this paper, we explore the ability of these algorithms to learn to interact with people. Via user study, we show that these algorithms alone do not consistently learn to collaborate with human partners. Similarly, we observe that humans fail to consistently collaborate with each other in the absence of explicit communication. However, we demonstrate that one algorithm does learn to effectively collaborate with people when paired with a novel cheap-talk communication system. In addition to this technical achievement, this work highlights the need to address AI and HRI synergistically rather than independently.