Robotic system design for reshaping estimated human intention in human-robot interactions

Thesis Type: Doctorate

Institution Of The Thesis: Middle East Technical University, Turkey

Approval Date: 2012

Thesis Language: English

Student: Akif Durdu



This thesis outlines the methodology and experiments associated with the reshaping of human intention via based on the robot movements in 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 significant branching in the field of human-robot interaction. In this thesis, we analyze how previously estimated human intentions change based on his/her actions by cooperating with mobile robots in a real human-robot environment. Our approach uses the Observable Operator Models (OOMs) and Hidden Markov Models (HMMs) designed for the intelligent mobile robotic systems, which consists of two levels: the low-level tracks the human 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 or HMM models to estimate human intention and decision making system to reshape the previously estimated human intention. Through this thesis, OOMs are started to be used at the human-robot interaction applications for first time. This two-level system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approaches are compared according to performance towards the degree of reshaping the detected intentions.