Unsupervised Online Grounding of Natural Language during Human-Robot Interactions
This addresses the challenge of enabling robots to understand synonyms and learn language connections in real-time during interactions, though it is incremental as it builds on prior grounding methods.
The paper tackles the problem of grounding natural language to percepts in human-robot interactions without supervision or offline training, proposing a cross-situational learning framework that updates mappings online. Results show it outperforms an existing unsupervised baseline in grounding words and phrases.
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.