Improved human-robot team performance through cross-training, an approach inspired by human team training practices

We design and evaluate a method of human-robot cross-training, a validated and widely used strategy for the effective training of human teams. Cross-training is an interactive planning method in which team members iteratively switch roles with one another to learn a shared plan for the performance of a collaborative task. We first present a computational formulation of the robot mental model, which encodes the sequence of robot actions necessary for task completion and the expectations of the robot for preferred human actions, and show that the robot model is quantitatively comparable to the mental model that captures the inter-role knowledge held by the human. Additionally, we propose a quantitative measure of robot mental model convergence and an objective metric of model similarity. Based on this encoding, we formulate a human-robot cross-training method and evaluate its efficacy through experiments involving human subjects (n = 60). We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training yields statistically significant improvements in quantitative team performance measures, as well as significant differences in perceived robot performance and human trust. Finally, we discuss the objective measure of robot mental model convergence as a method to dynamically assess human errors. This study supports the hypothesis that the effective and fluent teaming of a human and a robot may best be achieved by modeling known, effective human teamwork practices.