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Improving Grounded Natural Language Understanding through Human-Robot Dialog.
Jesse
Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick
Walker, Yuqian Jiang, Harel
Yedidsion, Justin Hart, Peter Stone,
and Raymond Mooney.
In Proceedings of the International Conference on
Robotics and Automation (ICRA 2019), May 2019.
Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically — continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.
@InProceedings{ICRA19-thomason,
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 Mooney},
title = {Improving Grounded Natural Language Understanding through Human-Robot Dialog},
booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA 2019)},
location = {Montreal, Canada},
month = {May},
year = {2019},
abstract = {
Natural language understanding for robotics can require substantial domain-
and platform-specific engineering. For example, for mobile robots to
pick-and-place objects in an environment to satisfy human commands, we can
specify the language humans use to issue such commands, and connect concept
words like red can to physical object properties. One way to alleviate this
engineering for a new domain is to enable robots in human environments to
adapt dynamically — continually learning new language constructions and
perceptual concepts. In this work, we present an end-to-end pipeline for
translating natural language commands to discrete robot actions, and use
clarification dialogs to jointly improve language parsing and concept
grounding. We train and evaluate this agent in a virtual setting on Amazon
Mechanical Turk, and we transfer the learned agent to a physical robot
platform to demonstrate it in the real world.
},
}
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