Measuring Variations in Workload during Human-Robot Collaboration through Automated After-Action Reviews

Zhiqin Qian, Liubove Orlov-Savko, Catherine Neubauer, Gregory Gremillion, Vaibhav V. Unhelkar

Abstract: Human collaborator's workload plays a central role in human-robot collaboration. Algorithms designed to minimize cognitive workload enhance fluent human-robot teamwork. Time series data of workload is vital for both designing and assessing these algorithms. However, accurately quantifying and measuring cognitive workload, particularly at high temporal resolution, poses a substantial challenge. Towards addressing this challenge, we explore the potential of after-action reviews (AARs) as a tool for gauging workload during human-robot collaboration. First, through a case study, we present and demonstrate AutoAAR for measuring human workload post-task at a high temporal resolution. Second, through a user study, we quantify the validity and utility of measurements derived using AutoAAR for human-robot teamwork. The paper concludes with guidelines and future directions to extend this method to measure other internal states, such as trust and intent.