Constructing policies for supportive behaviors and communicative actions in human-robot teaming

Current state-of-the-art robotic systems deployed in industry work in isolation from humans and do not allow for collaboration. Developing a robot that can work side-by-side with a human presents the advantage of allowing both the robot and the human worker to focus on the task each is best suited for, while assisting one another as needed. For the robot to provide assistive behavior to a human co-worker, it needs to learn what actions it should perform at each time step depending upon the state of the task. Such assistive actions are not intended to simply contribute to the completion of a particular task by instructing the robot to work on subtasks in isolation from the human worker; rather they are meant to help the worker complete the task more efficiently. As such, employing standard policy search or task and motion planning techniques is not sufficient to discover the supportive types of actions my system seeks to offer based on accurate estimations of the current task state. To this end, my research focuses on investigating policy search within hierarchical tasks that allow for two main abilities, namely helping the human co-worker more effectively complete a task and taking communicative actions that reduce state estimation uncertainty by asking the worker direct questions. The policy dictates what action the robot should take at each time step, based on inputs from a motion capture system providing observations about the configuration of the person's hands relative to the objects needed for accomplishing the task, as well as the person's answers to any questions posed by the robot.