Predicting Individual Human Performance in Human-Robot Teaming

Jack Kolb, Harish Ravichandar, Sonia Chernova

IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2021.


Coordinating human-robot teams requires careful planning and allocation of tasks to the most appropriate agents. This challenge is exacerbated by the fact that, unlike their robot teammates, humans exhibit significant variation in their abilities. Existing work largely ignores this variation in favor of simpler aggregate models, failing to leverage specialized capabilities of different individuals. In this work, we introduce simple cognitive tests for measuring inherent variations in human capabilities related to human-robot teaming, specifically, the ability to maintain situational awareness and to mentally model latent network structures. We then demonstrate that user study participant performance on these cognitive tests is correlated with, and thus is a predictor for, their performance on human-robot teaming tasks. These findings have the potential to improve human-robot teaming algorithms (e.g., task allocation) by providing a mechanism to better leverage individual differences in human agents.