A Model for Verbal and Non-Verbal Human-Robot Collaboration

We are motivated by building a system for an autonomous robot companion that collaborates with a human partner for achieving a common mission. The objective of the robot is to infer the human’s preferences upon the tasks of the mission so as to collaborate with the human by achieving human’s non-favorite tasks. Inspired by recent researches about the recognition of human’s intention, we propose a unified model that allows the robot to switch accurately between verbal and non-verbal interactions. Our system unifies an epistemic partially observable Markov decision process (POMDP) that is a human-robot spoken dialog system aiming at disambiguating the human’s preferences and an intuitive human-robot collaboration consisting in inferring human’s intention based on the observed human actions. The beliefs over human’s preferences computed during the dialog are then reinforced in the course of the task execution by the intuitive interaction. Our unified model helps the robot inferring the human’s preferences and deciding which tasks to perform to effectively satisfy these preferences. The robot is also able to adjust its plan rapidly in case of sudden changes in the human’s preferences and to switch between both kind of interactions. Experimental results on a scenario inspired from robocup@home outline various specific behaviors of the robot during the collabora-