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Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog.
Jesse
Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick
Walker, Yuqian Jiang, Harel
Yedidsion, Justin Hart, Peter Stone,
and Raymond J. Mooney.
The Journal of Artificial Intelligence Research
(JAIR), 67, February 2020.
In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to underlying semantic meanings and uses robotic sensors to create multi-modal models of perceptual concepts like red and heavy. The agent can be used for showing navigation routes, delivering objects to people, and relocating objects from one location to another. We use dialog clarification questions both to understand commands and to generate additional parsing training data. The agent employs opportunistic active learning to select questions about how words relate to objects, improving its understanding of perceptual concepts. We evaluated this agent on Amazon Mechanical Turk. After training on data induced from conversations, the agent reduced the number of dialog questions it asked while receiving higher usability ratings. Additionally, we demonstrated the agent on a robotic platform, where it learned new perceptual concepts on the fly while completing a real-world task.
@article{JAIR20-thomason,
title={Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog},
author={Jesse Thomason and Aishwarya Padmakumar and Jivko Sinapov and Nick Walker and Yuqian Jiang and Harel Yedidsion and Justin Hart and Peter Stone and Raymond J. Mooney},
journal={The Journal of Artificial Intelligence Research (JAIR)},
volume={67},
year={2020},
month={February},
abstract={In this work, we present methods for using human-robot dialog to
improve language understanding for a mobile robot agent. The agent parses
natural language to underlying semantic meanings and uses robotic sensors to
create multi-modal models of perceptual concepts like \emph{red} and
\emph{heavy}. The agent can be used for showing navigation routes,
delivering objects to people, and relocating objects from one location to
another. We use dialog clarification questions both to understand commands
and to generate additional parsing training data. The agent employs
opportunistic active learning to select questions about how words relate to
objects, improving its understanding of perceptual concepts. We evaluated
this agent on Amazon Mechanical Turk. After training on data induced from
conversations, the agent reduced the number of dialog questions it asked
while receiving higher usability ratings. Additionally, we demonstrated the
agent on a robotic platform, where it learned new perceptual concepts on the
fly while completing a real-world task.}
}
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