Development of an adaptive human-robot interaction system
One of the overarching goals of robotics research is that robots ultimately coexist with people in human societies as an integral part of them. In order to achieve this goal, robots need to be accepted by people as natural partners within the society. It is therefore essential for robots to have adaptive learning mechanisms that can intelligently update a human model for effective human-robot interaction (HRI). This might be critical in interactions with elderly and disabled people in their daily activities. This thesis has developed an intelligent HRI system that enables a mobile robot to learn adaptively about the behaviors and preferences of the people with whom it interacts. Various learning algorithms have been compared and a Bayesian learning mechanism has been implemented by estimating and updating a parameter set that models behaviors and preferences of people. Every time a user interacts with the robot, the model is updated. The robot then uses the model to predict future actions of its user. Existing speech recognition and navigation systems have been integrated with the learning system. The integrated system has been successfully implemented on a Pioneer 3-AT mobile robot.
"Development of an adaptive human-robot interaction system"
ETD Collection for Tennessee State University.