On the performance of hierarchical distributed correspondence graphs for efficient symbol grounding of robot instructions
On the performance of hierarchical distributed correspondence graphs for efficient symbol grounding of robot instructions
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机器人指令高效符号基础的分层分布式对应图的性能
DOI：
10.1109/iros.2015.7354117
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
T. Howard
中科院分区：
文献类型：
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作者：
Istvan Chung;Oron Y. Propp;Matthew R. Walter;T. Howard
自然语言界面是一种强大的工具，使人类和机器人能够在不需要大量培训或复杂图形界面的情况下传达信息。采用概率图形模型的统计技术已被证明在解释代表机器人方向跟随和对象操纵的命令和观察的符号方面是有效的。这些方法的一个局限是它们在处理更大和更复杂的符号表示时效率低下。在这里，我们提出了一个语言理解模型，使用解析树和环境模型来学习概率图形模型的结构，并在此学习的符号接地结构上进行推理。这个模型被称为分层分布式对应图（HDCG），它利用了语料库中表达的符号信息来构建搜索效率更高的最简图形模型。在一系列的比较实验中，我们证明了一个显着的效率提高，而不损失的准确性比当代方法的人机交互。
Natural language interfaces are powerful tools that enables humans and robots to convey information without the need for extensive training or complex graphical interfaces. Statistical techniques that employ probabilistic graphical models have proven effective at interpreting symbols that represent commands and observations for robot direction-following and object manipulation. A limitation of these approaches is their inefficiency in dealing with larger and more complex symbolic representations. Herein, we present a model for language understanding that uses parse trees and environment models both to learn the structure of probabilistic graphical models and to perform inference over this learned structure for symbol grounding. This model, called the Hierarchical Distributed Correspondence Graph (HDCG), exploits information about symbols that are expressed in the corpus to construct minimalist graphical models that are more efficient to search. In a series of comparative experiments, we demonstrate a significant improvement in efficiency without loss in accuracy over contemporary approaches for human-robot interaction.