Extracting Commonsense Knowledge for Robotic Agents from LLMs

The acquisition of commonsense knowledge remains a core challenge in both robotics and artificial intelligence. While Large Language Models (LLMs) encode rich latent knowledge, their unstructured and probabilistic nature limits their direct use in safety-critical domains like robotics. In this paper, we present a pipeline for extracting structured commonsense knowledge from LLMs and integrating it into a symbolic knowledge graph based on the Ontology for Robotic Knowledge Acquisition (ORKA). Grounded in the theory of conceptual spaces, our method targets physical object properties—both categorical (e.g., shape, material, location) and quantifiable (e.g., size, weight, temperature)—relevant to embodied reasoning. We evaluate the pipeline across a diverse set of LLMs, model sizes, and quantization levels, using human-annotated ground truth to assess accuracy. Results show that even compact and quantized models can produce reliable, interpretable knowledge. We also analyze the influence of measurement units and contextual relevance, highlighting trade-offs between model types and use cases. These results indicate that even smaller LLMs can serve as a useful intermediate source for deriving structured commonsense representations in robotics contexts.