Improving Grounded Natural Language Understanding through Human-Robot Dialog
Improving Grounded Natural Language Understanding through Human-Robot Dialog
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DOI：
10.1109/icra.2019.8794287
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Jesse Thomason;Aishwarya Padmakumar;Jivko Sinapov;Nick Walker;Yuqian Jiang;Harel Yedidsion;Justin W. Hart;P. Stone;R. Mooney
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文献类型：
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作者：
Jesse Thomason;Aishwarya Padmakumar;Jivko Sinapov;Nick Walker;Yuqian Jiang;Harel Yedidsion;Justin W. Hart;P. Stone;R. Mooney
自然对机器人技术的了解可能需要大量的域和平台特定的工程，例如，移动机器人在环境中拾取对象以满足人类的命令，我们可以指定人类用来发出此类命令的语言将红色概念词与物理对象属性相关联。在这项工作中学习新的语言构造和感知概念，我们提出了一个端到端的管道，用于转化自然语言命令以离散机器人动作，并使用澄清对话来共同改善语言解析和概念接地。在亚马逊机械土耳其人的虚拟环境中，我们将学习的代理转移到了物理机器人平台上，以在现实世界中证明它。
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.