What question did this study set out to answer?

The aim is to create a scalable architecture for generating trustworthy, explainable responses in autonomous robots.

April 28, 2026Open Access

Generating Trustworthy and Context-Aware Explanations for Autonomous Robots Using an LLM Agent-Based RAG Architecture

Puntos clave

The aim is to create a scalable architecture for generating trustworthy, explainable responses in autonomous robots.
Developed an agent-based Retrieval-Augmented Generation architecture for real-time data processing.
Conducted experiments on robotic navigation tasks to assess explanation performance metrics.
Utilized event streaming to capture high-volume data for accurate explanation generation.
Achieved Context Recall above 85%, Faithfulness over 78%, and Semantic Similarity near 96%.
Scored Correctness between 97.5% and 100% in evaluations.
Criteria for Understandability, Informativeness, and Coherence exceeded 4.0 on a 5-point scale.

Resumen

• Agent-Based RAG Architecture with Event-Driven Data for Explainable Robots. • A scalable, efficient, decoupled, and adaptive solution for robot explanations. • Review of XAI in robotics, LLMs in XARs, and LLM evaluation approaches. • Empirical results showing accurate robot explanations in various scenarios. • Future work on grounded explanations for autonomous agents. Effective communication in Human-Robot Interaction (HRI) is essential for building trust in autonomous systems. Robotic agents must provide clear, factual explanations that help non-expert users understand their decisions and actions, thereby promoting transparency and acceptance. However, generating structured, contextually relevant, and well-reasoned explanations remains a significant challenge, especially in dynamic environments, where rapidly changing circumstances make it difficult to ensure accuracy, consistency, and timeliness. To address this problem, we propose an architecture that generates natural language explanations grounded in accountable agent data. A distributed event streaming platform captures and processes high-volume system data in real time, which is then used by an agent-based Retrieval-Augmented Generation (RAG) approach to produce accurate and context-aware explanations. By decoupling explanation generation from the robot’s onboard resources, the architecture enables scalable and efficient reasoning while minimizing computational overhead. Experiments on robotic navigation tasks demonstrate that the system achieves high performance across quantitative metrics, with Context Recall consistently above 85%, Faithfulness over 78%, and Semantic Similarity near 96%. Criteria-based evaluations show high levels of Correctness (97.5-100%), with scores for Understandability, Informativeness, and Coherence exceeding 4.0 on a 5-point scale. These results provide strong evidence that integrating curated real-time data with agent-based reasoning enhances the interpretability, reliability, and user trust in autonomous robot behavior.

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