Building language-agnostic grounded language learning systems

Abstract

Learning the meaning of grounded language— language that references a robot’s physical environment and perceptual data—is an important and increasingly widely studied problem in robotics and human-robot interaction. However, with a few exceptions, research in robotics has focused on learning groundings for a single natural language pertaining to rich perceptual data. We present experiments on taking an existing natural language grounding system designed for English and applying it to a novel multilingual corpus of descriptions of objects paired with RGB-D perceptual data. We demonstrate that this specific approach transfers well to different languages, but also present possible design constraints to consider for grounded language learning systems.