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When interacting in unstructured human environments, occasional robotfailures are inevitable. When such failures occur, everyday people, rather thantrained technicians, will be the first to respond. Existing natural languageexplanations hand-annotate contextual information from an environment to helpeveryday people understand robot failures. However, this methodology lacksgeneralizability and scalability. In our work, we introduce a moregeneralizable semantic explanation framework. Our framework autonomouslycaptures the semantic information in a scene to produce semanticallydescriptive explanations for everyday users. To generate failure-focusedexplanations that are semantically grounded, we leverages both semantic scenegraphs to extract spatial relations and object attributes from an environment,as well as pairwise ranking. Our results show that these semanticallydescriptive explanations significantly improve everyday users' ability to bothidentify failures and provide assistance for recovery than the existingstate-of-the-art context-based explanations.

Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures