  Robots performing human-scale manipulation tasks require an extensive amount
of knowledge about their surroundings in order to perform their actions
competently and human-like. In this work, we investigate the use of virtual
reality technology as an implementation for robot environment modeling, and
present a technique for translating scene graphs into knowledge bases. To this
end, we take advantage of the Universal Scene Description (USD) format which is
an emerging standard for the authoring, visualization and simulation of complex
environments. We investigate the conversion of USD-based environment models
into Knowledge Graph (KG) representations that facilitate semantic querying and
integration with additional knowledge sources.
