Ontology-Guided Reasoning for Affordance-Based Explanations of Robot Navigation

2026-05-27

AI Summary

When robots operating in human environments encounter obstacles, they often struggle to interpret how the functional properties of objects and their state changes affect navigability, hindering the generation of plausible and actionable explanations. This work proposes a novel approach based on a local functional affordance ontology to model entities, their functional attributes, states, and spatial relationships in the environment. By reasoning over this representation, the system evaluates hypothetical object–state alterations as potential explanatory factors for navigation failures. To the best of our knowledge, this is the first method to leverage a functional affordance ontology for generating action-oriented navigation explanations, moving beyond conventional approaches that rely solely on semantic labels. Experiments on the Robot Librarian benchmark demonstrate that the proposed method more accurately identifies critical explanatory factors than purely semantic baselines and maintains robustness even under increased semantic interference.

Abstract

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.

Problem

• affordance
• robot navigation
• explainability
• ontology
• reasoning

Innovation

• ontology-guided reasoning
• affordance-based explanation
• robot navigation
• semantic grounding
• actionable explanation