HEXAR: a Hierarchical Explainability Architecture for Robots

2026-01-06

arXiv.org

AI Summary

This work addresses the challenge of balancing high-level behavioral understanding with modular architecture in robot explainability, a trade-off that often confines existing methods to either overly localized or excessively holistic explanations. To overcome this limitation, the authors propose a plug-and-play hierarchical explainability framework that equips individual robot modules with dedicated explainers and employs an explainer selector to dynamically orchestrate query-specific optimal explanations. For the first time, this framework integrates modularity, hierarchy, and diverse explanation techniques—including large language model (LLM) reasoning, causal models, and feature importance—enabling precise, efficient, and customizable interpretations of complex robotic behaviors. Evaluated on TIAGo assistive tasks for elderly care, the approach significantly outperforms end-to-end and aggregated baselines in root-cause identification, erroneous information filtering, and computational efficiency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• explainability
• robotics
• modular architecture
• hierarchical explanation
• autonomous systems

Innovation

• Hierarchical Explainability
• Modular Explanation
• Explainer Selector
• Robot Transparency
• Explainable AI for Robotics