A Dialogue Control Model Based on Ambiguity Evaluation of Users’ Instructions and Stochastic Representation of Experiences

Abstract

To operate in everyday environments, robots much accomplish complex tasks following often mbiguous and uncertain instructions, mainly through advanced inference or recognition. We focus on an intelligent human-robot interaction framework that reduces the burden of the user. Robots compensate for ambiguities by active sensing and dialogue control through questions and suggestions to users. Robots also use experience to reduce the user’s burden. We propose a criterion for ambiguity evaluation of user instructions, stochastic representation of personal experiences and a dialogue control model for accomplishing tasks in complex environments. We demonstrate the feasibility of our proposal in demonstrate experiment where a robot searches for an object in disorganized work space with ambiguous instructions.