Grounded Semantic Role Labelling from Synthetic Multimodal Data for Situated Robot Commands

Understanding natural language commands in situated Human-Robot Interaction (HRI) requires linking linguistic input to perceptual context. Traditional symbolic parsers lack the flexibility to operate in complex, dynamic environments. We introduce a novel Multimodal Grounded Semantic Role Labelling (G-SRL) framework that combines frame semantics with perceptual grounding, enabling robots to interpret commands via multimodal logical forms. Our approach leverages modern Vision Language Models (VLMs), which jointly process text and images, and is supported by an automated pipeline that generates high-quality training data. Structured command annotations are converted into photorealistic scenes via LLM-guided prompt engineering and diffusion models, then rigorously validated through object detection and visual question answering. The pipeline produces over 11,000 imagecommand pairs (3,500+ manually validated), while approaching the quality of manually curated datasets at significantly lower cost.