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arXiv · 2601.03070

HEXAR: a Hierarchical Explainability Architecture for Robots

Tamlin Love·Ferran Gebellí·Pradip Pramanick·Antonio Andriella·Guillem Alenyà·Anais Garrell·Raquel Ros·Silvia Rossi
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Abstract

As robotic systems become increasingly complex, the need for explainable decision-making becomes critical. Existing explainability approaches in robotics typically either focus on individual modules, which can be difficult to query from the perspective of high-level behaviour, or employ monolithic approaches, which do not exploit the modularity of robotic architectures. We present HEXAR (Hierarchical EXplainability Architecture for Robots), a novel framework that provides a plug-in, hierarchical approach to generate explanations about robotic systems. HEXAR consists of specialised component explainers using diverse explanation techniques (e.g., LLM-based reasoning, causal models, feature importance, etc) tailored to specific robot modules, orchestrated by an explainer selector that chooses the most appropriate one for a given query. We implement and evaluate HEXAR on a TIAGo robot performing assistive tasks in a home environment, comparing it against end-to-end and aggregated baseline approaches across 180 scenario-query variations. We observe that HEXAR significantly outperforms baselines in root cause identification, incorrect information exclusion, and runtime, offering a promising direction for transparent autonomous systems.

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Tamlin Love, Ferran Gebellí, Pradip Pramanick, Antonio Andriella, Guillem Alenyà, Anais Garrell, Raquel Ros, Silvia Rossi. 2026-01-06. HEXAR: a Hierarchical Explainability Architecture for Robots. https://arxiv.org/abs/2601.03070

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