Evaluating Human Behavior in Manual and Shared Control via Inverse Optimization

Abstract

Shared control systems have a great potential to contribute to a safer human-machine interaction. A great body of literature has been concerned with the design of the automation the human is sharing control with. At the same time, an adequate design is connected to the availability of reliable models of human behavior. A promising modeling approach is given by optimal control theory, where human behavior arises from the minimization of a cost function. However, most of the work found in literature focus on determining the cost function of the human in a situation without any haptic interaction with a partner, i.e. in manual control tasks. Motivated by several studies which indicate that human behavior changes when completing a task cooperatively, this paper proposes an optimal control approach for human behavior modeling in a shared control scenario. We further hypothesize that the human cost functions in a shared control scenario change significantly when compared to the ones which arise from a human performing a control task alone. We apply an inverse optimization approach in order to identify the cost function in both scenarios. In order to evaluate our hypothesis, a study was conducted where 42 participants performed a tracking task in a manual mode and then sharing control with an assistance system. The findings show that the model is able to describe human behavior in both shared and manual control. Furthermore, the results confirm that the human cost function changes considerably between both scenarios.