Using Semantic Models for Robust Natural Language Human Robot Interaction

While robotic platforms are moving from industrial to consumer applications, the need of flexible and intuitive interfaces becomes more critical and the capability of governing the variability of human language a strict requirement. Grounding of lexical expressions, i.e. mapping words of a user utterance to the perceived entities of a robot operational scenario, is particularly critical. Usually, grounding proceeds by learning how to associate objects categorized in discrete classes (e.g. routes or sets of visual patterns) to linguistic expressions. In this work, we discuss how lexical mapping functions that integrate Distributional Semantics representations and phonetic metrics can be adopted to robustly automate the grounding of language expressions into the robotic semantic maps of a house environment. In this way, the pairing between words and objects into a semantic map facilitates the grounding without the need of an explicit categorization. Comparative measures demonstrate the viability of the proposed approach and the achievable robustness, quite crucial in operational robotic settings.