Human-in-the-Loop Uncertainty Analysis in Self-Adaptive Robots Using LLMs

TL;DR

This paper presents RoboULM, a human-in-the-loop tool leveraging large language models to systematically identify and analyze uncertainties in self-adaptive robots during the design phase.

Contribution

It introduces RoboULM, a novel methodology and tool that uses LLMs for structured uncertainty exploration and provides an uncertainty taxonomy for self-adaptive robots.

Findings

Practitioners found RoboULM useful and easy to understand.

Structured prompting and iterative refinement were highly valued.

Evaluation with 16 practitioners across four industrial cases confirmed RoboULM's effectiveness.

Abstract

Self-adaptive robots operate in dynamic, unpredictable environments where unaddressed uncertainties can lead to safety violations and operational failures. However, systematically identifying and analyzing these uncertainties, including their sources, impacts, and mitigation strategies, remains a significant challenge given the inherent complexity of real-world environments, dynamic robotic behavior, and the rapid evolution of robotic technologies. To address this, we introduce RoboULM, a human-in-the-loop methodology and tool that supports practitioners in systematically exploring uncertainties at the design stage using large language models (LLMs). Moreover, we present an uncertainty taxonomy that provides a detailed catalog of uncertainties in self-adaptive robots. We evaluated RoboULM with 16 practitioners from four industrial use cases. The results show that RoboULM was perceived as both useful and easy to understand, with the participants particularly valuing structured prompting and iterative refinement support. These findings demonstrate the potential of RoboULM as a viable solution for systematic uncertainty analysis in complex robots.