Understanding Human Gaze as a Nonverbal Communication Cue in Human-Robot Interaction

• Main Authors: Giovanni Ventilii, 馮笛
• Other Authors: Han-Pang Huang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/dn5n6a

碩士 === 國立臺灣大學 === 機械工程學研究所 === 106 === Human eyes represent a strong non-verbal communication tool: eye gaze not only gives cues about interest, attention and intention of people, but also manages several kinds of social face-to-face interaction. Moreover, people unconsciously but rigorously follow specific unwritten rules when directing their gaze during social interactions. When it comes to robots, however, only few applications take into account human gaze in Human-Robot Interaction (HRI), focusing on some specific aspects or scenarios. This thesis aims to create a comprehensive intelligent system to automatically sense, understand and react to human eye gaze, in order to both improve HRI smoothness and make robots behave more human-likely. The online system, mounted on a mobile robot, detects and tracks the gaze of humans from 2D images based on a Convolutional Neural Network (CNN), it then uses a novel incremental variant of Hidden Markov Model (iCHMM) to estimate the intention of the person with whom the interaction is taking place. Finally, with this information, the robot acts accordingly to its own intention. The system has been proved to have an overall accuracy greater than 80% in correctly estimate the intention of people. The robot both increased the success rate of interaction establishment during human approaching and decreased turn taking mistakes in conversations. It was also proved to be effective in raising the overall quality of user experience during HRI.