Morphing Estimated Human Intention via Human-Robot Interactions

Estimating and reshaping human intentions are topics of research in the field of human-robot interaction. Although works on estimating human intentions are quite well known research areas in the literature, reshaping intentions through interactions is a new significant branching in the field of human-robot interaction. In this paper, we research how the human intentions change based on his/her actions by moving the robots in a real human-robot environment. Our approach uses the Hidden Markov Model (HMM) tailored for the intelligent robotic systems. The algorithmic design consists of two phases: human tracking and the use of intelligent robots that aims to change intentions of individuals in the environment. In the former phase, postures and locations of the human are monitored by applying low-level video processing methods. The latter phase learned HMM models are used to reshape the estimated human intention. This two-phase system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approach is discussed according to performance towards the '' degree '' of reshaping the detected intentions.