User Skill Assessment Using Informative Interfaces for Personalized Robot-Assisted Training

Abstract

Robot Assisted Training (RAT) systems have been successfully deployed to provide assistance during a training task, promoting an efficient interaction with the user. Personalization can improve the efficiency of the interaction and thus enhance the effects of the training session. Personalization can be achieved through user skill assessment in order to choose an appropriate robot behavior that matches user abilities and needs. Graphical User Interfaces have been used to enable human supervisors to control robots and guide the interaction in RAT-based systems. This work focuses on how such interfaces can be used to enable human supervisor users (e.g., therapists) to assess user skills during a robot-based cognitive task. In this study, we investigate how different visualization features affect decision making and efficiency, towards the design of an intelligent and informative interface.