Work

Identifying Uncertainty in Self-Adaptive Robotics With Large Language Models

Year: 2025

Type: article

Abstract: Future self-adaptive robots are expected to operate in highly dynamic environments while effectively managing uncertainties. However, identifying the sources and impacts of uncertainties in such robotic systems and defining appropriate mitigation strategies is challenging due to the inherent complexity of self-adaptive robots and the lack of comprehensive knowledge about the various factors influencing uncertainty. Hence, practitioners often rely on intuition and past experiences from similar systems to address uncertainties. In this article, we evaluate the potential of large language models (LLMs) in enabling a systematic and automated approach to identify uncertainties in self-adaptive robotics throughout the software engineering lifecycle. For this evaluation, we analyzed 10 advanced LLMs with varying capabilities across four industrial-sized robotic case studies, gathering the practitioners’ perspectives on the LLM-generated responses related to uncertainties. Results showed that practitioners agreed with 63–88% of the LLM responses and expressed strong interest in the practicality of LLMs for this purpose.

Source: IEEE Software

DOI: 10.1109/ms.2025.3620578

Authors: Hassan Sartaj, Jalil Boudjadar, Mirgita Frasheri, Shaukat Ali, Peter Gorm Larsen

Institutions: Simula Research Laboratory, Aarhus University

Language: English

Topic: Advanced Software Engineering Methodologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,995

Funder: HORIZON EUROPE Framework Programme