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In the context of human-supervised autonomy, we study the problem of optimalfidelity selection for a human operator performing an underwater visual searchtask. Human performance depends on various cognitive factors such as workloadand fatigue. We perform human experiments in which participants perform twotasks simultaneously: a primary task, which is subject to evaluation, and asecondary task to estimate their workload. The primary task requiresparticipants to search for underwater mines in videos, while the secondary taskinvolves a simple visual test where they respond when a green light displayedon the side of their screens turns red. Videos arrive as a Poisson process andare stacked in a queue to be serviced by the human operator. The operator canchoose to watch the video with either normal or high fidelity, with normalfidelity videos playing at three times the speed of high fidelity ones.Participants receive rewards for their accuracy in mine detection for eachprimary task and penalties based on the number of videos waiting in the queue.We consider the workload of the operator as a hidden state and model theworkload dynamics as an Input-Output Hidden Markov Model (IOHMM). We use aPartially Observable Markov Decision Process (POMDP) to learn an optimalfidelity selection policy, where the objective is to maximize total rewards.Our results demonstrate improved performance when videos are serviced based onthe optimal fidelity policy compared to a baseline where humans choose thefidelity level themselves.

Optimal Fidelity Selection for Improved Performance in Human-in-the-Loop Queues for Underwater Search