Measuring State Utilization During Decision Making in Human-Robot Teams

Efficient team design necessitates a comprehensive understanding of human factors, encompassing abilities, limitations, and internal states. In human-robot teaming research, recent efforts explore integrating emotions, workload, fatigue, and stress into decision-making using deep reinforcement learning. Despite promising results, the black-box nature of these algorithms raises questions about the consistent reliance on human internal states or their consideration as information or noise in the decision-making process. This study introduces a state utilization (SU) metric to measure the reliance of reinforcement-based agents on each state feature. This metric is validated on data from the Cartpole environment by OpenAI and a human-robot teaming experiment using NASA MATB-II environment. The SU provides insight into the relevance and usage of state features and human data modalities by the robot, showing clear trends based on the nature of the tasks and offering an understanding of why the RL agent takes certain actions. This, in turn, enhances the explainability of the RL agent's policy used for human robot teaming.