Modelling of robot attention demand in human-robot interaction using finite fuzzy state automata

Many systems have been implemented towards achieving effective human-machine interaction, but run the risk of being ignored if appropriate performance metrics are not in place. As a result, our goal becomes that of providing a foundation upon which we can assess how well the human and the robot perform as a team. Toward the efficient modelling of such metrics, we attempt to determine the true amount of time that an operator has to dedicate to the robot. Therefore, we define the robot attention demand (RAD) as a function of both direct interaction time (DIT) and indirect interaction time (IIT), where the IIT is a direct consequence of the human trust in automation. We propose a two-level fuzzy temporal model to evaluate the human trust in automation while collaborating with robots to complete some tasks. The model combines the advantages of fuzzy logic and finite state machines to best model this phenomenon, and reduces the system complexity and the size of the knowledge base by grouping perceptions into first- and second-order perceptions. The fuzzy knowledge base is further updated by implementing an application robotic platform where robots and users interact via natural language to complete tasks with varying levels of complexity. User feedback is noted and used to tune the knowledge base where needed.