Human-Aware Reinforcement Learning for Adaptive Human Robot Teaming

Abstract

Mistakes in high stress and critical multitasking environments, such as piloting an airplane and the NASA control room, can lead to catastrophic failures. The human's internal state (e.g., workload) may be used to facilitate a robot teammate's adaptations, such that the robot can interact with the human without negatively impacting overall team performance. Human performance has a direct correlation with workload states; thus, the human's internal workload state may be leveraged to adapt a robot's interactions with the human in order to improve team performance. A reinforcement learning-based paradigm that incorporates human workload states to determine appropriate robot adaptations is presented. Preliminary results using the proposed approach in a supervisory-based NASA MATB-II environment are presented.