  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.
