Towards Understanding Language through Perception in Situated Human-Robot Interaction: From Word Grounding to Grammar Induction
This addresses the challenge of language understanding in human-robot interaction, but it appears incremental as it builds on existing grounding and grammar induction methods.
The paper tackles the problem of inferring latent grammatical structure in language for robots, aiming to ground parts of speech through visual perception and induce Combinatory Categorial Grammar for phrases to enable appropriate understanding of human instructions.
Robots are widely collaborating with human users in diferent tasks that require high-level cognitive functions to make them able to discover the surrounding environment. A difcult challenge that we briefy highlight in this short paper is inferring the latent grammatical structure of language, which includes grounding parts of speech (e.g., verbs, nouns, adjectives, and prepositions) through visual perception, and induction of Combinatory Categorial Grammar (CCG) for phrases. This paves the way towards grounding phrases so as to make a robot able to understand human instructions appropriately during interaction.