Observable operator models for reshaping estimated human intention by robot moves in human-robot interactions

This paper outlines the methodology and experiments associated with the reshaping of human intention based on robot movements during Human-Robot Interactions (HRI). Although works on estimating human intentions are quite well known research areas in the literature, reshaping intentions through interactions is a new branching of the human-robot interaction field beginning to gain significance. In this paper, we analyze how previously estimated human intentions change based on his/her cooperation with mobile robots in a real human-robot environment. Our approach uses the Observable Operator Models (OOMs) in two levels: the low-level tracks individuals for which their initial intentions are detected while the high-level guides the mobile robots into moves that aim to change intentions of individuals in the environment. In the low level, postures and locations of the human are monitored by applying image processing methods. The high level uses an algorithm which includes learned OOM models to estimate the initial human intention and a decision making system to reshape the previously estimated human intention. The novelty of this paper does not only come from the originality of the intention reshaping concept through robot moves, but this paper also initiates the use, in the literature, of OOMs in the human-robot interaction applications. The two-level system developed is tested on videos taken from human-robot environment. The results obtained using the proposed approach are discussed according to performance based on the “degree” of reshaping of the detected intentions.