Learning actions from human-robot dialogues

R. Cantrell, P. Schermerhorn, Matthias Scheutz
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引用次数: 46

Abstract

Natural language interactions between humans and robots are currently limited by many factors, most notably by the robot's concept representations and action repertoires. We propose a novel algorithm for learning meanings of action verbs through dialogue-based natural language descriptions. This functionality is deeply integrated in the robot's natural language subsystem and allows it to perform the actions associated with the learned verb meanings right away without any additional help or learning trials. We demonstrate the effectiveness of the algorithm in a scenario where a human explains to a robot the meaning of an action verb unknown to the robot and the robot is subsequently able to carry out the instructions involving this verb.
从人机对话中学习动作
人类和机器人之间的自然语言交互目前受到许多因素的限制,最明显的是机器人的概念表示和动作库。我们提出了一种基于对话的自然语言描述学习动作动词意义的新算法。该功能深度集成在机器人的自然语言子系统中,允许它立即执行与学习到的动词含义相关的动作,而无需任何额外的帮助或学习试验。我们在一个场景中展示了该算法的有效性,在这个场景中,人类向机器人解释机器人未知的动作动词的含义,机器人随后能够执行涉及该动词的指令。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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