Situated natural language interaction in uncertain and open worlds
Natural language understanding and generation capabilities are crucial
for natural human-like human robot interactions. This is especially
true in domains such as eldercare, education, space, and
search-and-rescue robotics, in which alternate interfaces or
interaction techniques may be difficult for users to use due to
cognitive or physical limitations. Approximately 40% of wheelchair
users, for example, find it difficult or impossible to use a standard
joystick, making natural language an attractive modality for
interaction and control.
My research investigates how intelligent robots can communicate through natural language in realistic human-robot interaction scenarios, in which knowledge is uncertain, incomplete, and decentralized. To do so, I draw on techniques and concepts from artificial intelligence, psychology, linguistics, and philosophy, and engage in both algorithm development and empirical experimentation.
In this dissertation defense, I will present a set of cognitively inspired algorithms I have developed to allow robots to better identify the entities (e.g., objects, people, and locations) referenced in natural language by their human conversational partners, and to better infer those conversational partners’ intentions, in uncertain and open worlds. I will then discuss how these algorithms have been implemented on a robotic wheelchair in order to significantly extend the state of the art of natural language enabled robot wheelchairs.