Toward a Probabilistic Approach to Acquiring Information from Human Partners Using Language
Our goal is to build robots that can robustly interact with humans using natural language. This problem is extremely challenging because human language is filled with ambiguity, and furthermore, the robot's model of the environment might be much more limited than the human partner. When humans encounter ambiguity in dialog with each other, a key strategy to resolve it is to ask clarifying questions about whatthey do not understand. This paper describes an approach for enabling robots to take the same approach: asking the human partner clarifying questions about ambiguous commands in order to infer better actions. The robot fuses information from the command, the question, and the answer by creating a joint probabilistic graphical model in the Generalized Grounding Graph framework. We demonstrate that by performing inference using information from the command, question and answer, the robot is able to infer object groundings and follow commands with higher accuracythan by using the command alone.