人間とロボットの対話における自然言語の教師なしオンライン接地
Unsupervised Online Grounding of Natural Language during Human-Robot Interactions
人間とロボットの対話中に、単語と知覚の対応を教師なし・オンラインで学習するクロスシチュエーション学習型の接地フレームワークを提案し、既存手法より優れることを実験で示した。
著者: Oliver Roesler
分類: cs.CL, cs.AI, cs.LG, cs.RO
原文アブストラクト
Allowing humans to communicate through natural language with robots requires connections between words and percepts. The process of creating these connections is called symbol grounding and has been studied for nearly three decades. Although many studies have been conducted, not many considered grounding of synonyms and the employed algorithms either work only offline or in a supervised manner. In this paper, a cross-situational learning based grounding framework is proposed that allows grounding of words and phrases through corresponding percepts without human supervision and online, i.e. it does not require any explicit training phase, but instead updates the obtained mappings for every new encountered situation. The proposed framework is evaluated through an interaction experiment between a human tutor and a robot, and compared to an existing unsupervised grounding framework. The results show that the proposed framework is able to ground words through their corresponding percepts online and in an unsupervised manner, while outperforming the baseline framework.
