Proactive planning using a hybrid temporal influence diagram for human assistive robots

Abstract

For a robot to interact with a person effectively, it needs to predict future events that will be caused by the person to occur. By predicting events, a robot can take some preparative actions to reduce the waiting time and greatly improve the interaction. To select the best proactive actions and the best times for those actions, we propose a hybrid temporal influence diagram that can make proactive plans that minimize expected waiting times in a human-robot interaction. To validate our proposed method, we show experimental results for a robotic assistant in a manual assembly task.