Robotics: Science and Systems VIII

Toward Information Theoretic Human-Robot Dialog

Stefanie Tellex, Pratiksha Thaker, Robin Deits, Thomas Kollar, Nicholas Roy


Our goal is to build robots that can robustly interact with humans using natural language. This problem is challenging because human language is filled with ambiguity, and furthermore, due to limitations in sensing, the robot's perception of its environment might be much more limited than that of its human partner. To enable a robot to recover from a failure to understand a natural language utterance, this paper describes an information-theoretic strategy for asking targeted clarifying questions and using information from the answer to disambiguate the language. To identify good questions, we derive an estimate of the robot's uncertainty about the mapping between specific phrases in the language and aspects of the external world. This metric enables the robot to ask a targeted question about the parts of the language for which it is most uncertain. After receiving an answer, the robot fuses information from the command, the question, and the answer in a joint probabilistic graphical model in the



    AUTHOR    = {Stefanie Tellex AND Pratiksha Thaker AND Robin Deits AND Thomas Kollar AND Nicholas Roy}, 
    TITLE     = {Toward Information Theoretic Human-Robot Dialog}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2012}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July},
    DOI       = {10.15607/RSS.2012.VIII.052}