(Georgia Institute of Technology, 2009-11-10)
Wagner, Alan Richard
Can a robot understand a human's social behavior? Moreover, how should a robot act in response to a human's behavior? If the goals of artificial intelligence are to understand, imitate, and interact with human level intelligence then researchers must also explore the social underpinnings of this intellect. Our endeavor is buttressed by work in biology, neuroscience, social psychology and sociology. Initially developed by Kelley and Thibaut, social psychology's interdependence theory serves as a conceptual skeleton for the study of social situations, a computational process of social deliberation, and relationships (Kelley&Thibaut, 1978). We extend and expand their original work to explore the challenge of interaction with an embodied, situated robot.
This dissertation investigates the use of outcome matrices as a means for computationally representing a robot's interactions. We develop algorithms that allow a robot to create these outcome matrices from perceptual information and then to use them to reason about the characteristics of their interactive partner. This work goes on to introduce algorithms that afford a means for reasoning about a robot's relationships and the trustworthiness of a robot's partners. Overall, this dissertation embodies a general, principled approach to human-robot interaction which results in a novel and scientifically meaningful approach to topics such as trust and relationships.
(Georgia Institute of Technology, 2007-11-12)
Martinson, Eric Beowulf
With the growth of successes in pattern recognition and signal processing, mobile robot applications today are increasingly equipping their hardware with microphones to improve the set of available sensory information. However, if the robot, and therefore the microphone, ends up in a poor location acoustically, then the data will remain noisy and potentially useless for accomplishing the required task. This is compounded by the fact that there are many bad acoustic locations through which a robot is likely to pass, and so the results from auditory sensors often remain poor for much of the task.
The movement of the robot, though, can also be an important tool for overcoming these problems, a tool that has not been exploited in the traditional signal processing community. Robots are not limited to a single location as are traditionally placed microphones, nor are they powerless over to where they will be moved as with wearable computers. If there is a better location available for performing its task, a robot can navigate to that location under its own power. Furthermore, when deciding where to move, robots can develop complex models of the environment. Using an array of sensors, a mobile robot can build models of sound flow through an area, picking from those models the paths most likely to improve performance of an acoustic application.
In this dissertation, we address the question of how to exploit robotic movement. Using common sensors, we present a collection of tools for gathering information about the auditory scene and incorporating that information into a general framework for acoustical awareness. Thus equipped, robots can make intelligent decisions regarding control strategies to enhance their performance on the underlying acoustic application.