Generating Actionable Robot Knowledge Bases by Combining 3D Scene Graphs with Robot Ontologies

-- In robotics, the effective integration of environmental data into actionable knowledge remains a significant challenge due to the variety and incompatibility of data formats commonly used in scene descriptions, such as MJCF, URDF, and SDF . This paper presents a novel approach that addresses these challenges by developing a unified scene graph model that standardizes these varied formats into the Universal Scene Description (USD) format. We evaluated our approach by converting procedural 3D environments into USD format, which is then annotated semantically and translated into a knowledge graph to effectively answer competency questions, demonstrating its utility for real-time robotic decision-making. Additionally, we developed a web-based visualization tool to support the semantic mapping process, providing users with an intuitive interface to manage the 3D environment. In AI-powered and cognition-enabled robotics, robot agents face the challenge of fulfilling underdetermined task requests such as "prepare a breakfast" or "bring me something to drink." To accomplish these tasks, robots must infer the specific body movements required, which heavily depend on the given environment and the robot's knowledge and reasoning capabilities. This knowledge includes the physics, geometry, and visual characteristics of the environment and its objects. Although the necessary details for computing these movements are contained within virtual reality environments' scene graph data structures, these structures are not standardised, inherently machine-understandable, or interpretable. This limitation restricts a robot's ability to answer task-critical queries in changing environments, such as whether milk is stored within a container, how to operate a refrigerator or the outcomes of handling a milk carton by the lid.