A quantitative measure of regret in decision-making for human-robot collaborative search tasks

Human-robot collaborations (HRC) can be used for object detection in domain search tasks, which integrate human and computer vision to improve accuracy and efficiency. The Bayesian sequential decision-making (BSD) method has been used for task allocation of a robot in search tasks. In this paper, we first provide an explanation to reveal the nature of the BSD approach: it makes decisions based on the expected value criterion, which is proved to be very different from human decision-making behaviors. On the other hand, it has been shown that joint performance of a team will improve if all members share the same decision-making logic. In HRC, since forcing a human to act like a robot is not desired, we propose to modify the BSD approach such that the robot imitates human logic. In particular, regret theory qualitatively models human's rational decision-making behaviors under uncertainty. We propose a holistic framework to measure regret quantitatively, an individual-based parametric model that fits the measurements, and the integration of regret into the BSD method. Furthermore, we design a human-in-the-loop experiment based on the framework to collect enough data points to further elicit requisite functions of regret theory. Our preliminary results match all the properties in regret theory, while the parametric elicited model shows a good fit to the experimental data.