Unveiling the Dynamics of Human Decision-Making: From Strategies to False Beliefs in Collaborative Human-Robot Co-Learning Tasks

Abstract

As robots become more integrated into humans' daily activities, it is essential to understand how human decision varies during co-learning with robots in real-world scenarios. Despite great advances in developing humanoid robots, which aims to foster a seamless collaborative world where humans and robots coexist, a gap remains in the social bond between humans and robots, particularly in tasks demanding optimal teamwork. In alignment with current pioneering efforts in the human-robot collaboration field, this paper presents an experimental study leading to a rationale analysis and classification of human behavioral dynamics during a joint collaborative pick-and-place task with a robotic arm. Our post-experimental analysis categorized human behavioral dynamics into three distinct broad categories, which are "strategic explorers and decoders", "reactive navigators and dynamic responders", and "score maximizers and ideal collaborators". We provide in-depth analysis for each group, exploring potential reasons for their observed behavioral patterns and irrational decisions substantiated by intuitions from psychological and behavioral game theory, including concepts of false belief and strategy development.