Ontology-Guided Reasoning for Affordance-Based Explanations of Robot Navigation
For robots operating in human environments, this work provides a method to generate actionable explanations by reasoning about object affordances, improving explainability and reliability.
The paper proposes ontology-guided reasoning for affordance-based explanations of robot navigation, showing that it identifies relevant explanation factors more accurately than a semantic-only baseline and remains robust as semantic clutter increases.
This paper proposes ontology-guided reasoning for affordance-based explanations of robot navigation. In human environments, it is not sufficient for a robot to detect that its route is blocked. It must also reason about what nearby objects afford, which state changes are possible, and which of these changes would allow it to continue safely. We address this problem by representing nearby entities, their affordances, affordance states, and qualitative spatial relations in a local affordance ontology and by evaluating hypothetical object--affordance state changes as candidate explanation factors. This yields explanations that are not only semantically grounded but also actionable. We instantiate the approach in a lightweight benchmark centered on a robot librarian scenario and evaluate it on procedurally generated navigation cases. The results show that ontology-guided reasoning identifies relevant explanation factors more accurately than a semantic-only baseline and remains robust as semantic clutter increases. Overall, the paper argues that affordance ontologies can serve not merely as semantic descriptions of the environment, but as reasoning foundations for explainability and reliable robot autonomy.