Inferring the Human's Objective in Human Robot Interaction

This thesis discusses the use of Bayesian Inference in inferring over the human's objective for Human-Robot Interaction, more specifically, it focuses upon the adaptation of methods to better utilize the information for inferring upon the human's objective for Reward Learning and Communicative Shared Autonomy settings. To accomplish this, we first examine state-of-the-art methods for approaching Bayesian Inverse Reinforcement learning where we explore the strengths and weaknesses of current approaches. After which we explore alternative methods for approaching the problem, borrowing similar approaches to those of the statistics community to apply alternative methods to improve the sampling process over the human's belief. After this, I then move to a discussion on the setting of Shared Autonomy in the presence and absence of communication. These differences are then explored in our method for inferring upon an environment where the human is aware of the robot's intention and how this can be used to dramatically improve the robot's ability to cooperate and infer upon the human's objective. In total, I conclude that the use of these methods to better infer upon the human's objective significantly improves the performance and cohesion between the human and robot agents within these settings.