Identifying Uncertainty in Self-Adaptive Robotics with Large Language Models

• URL: http://arxiv.org/abs/2504.20684v1
• Date: Tue, 29 Apr 2025 12:07:39 GMT
• Title: Identifying Uncertainty in Self-Adaptive Robotics with Large Language Models
• Authors: Hassan Sartaj, Jalil Boudjadar, Mirgita Frasheri, Shaukat Ali, Peter Gorm Larsen,
• Abstract summary: We evaluate the potential of large language models (LLMs) in enabling a systematic approach to identify uncertainties in self-adaptive robotics.<n>We analyzed 10 advanced LLMs with varying capabilities across four industrial-sized robotics case studies.<n>Results showed that practitioners agreed with 63-88% of the LLM responses and expressed strong interest in the practicality of LLMs for this purpose.
• Score: 4.638192191684079
• License: http://creativecommons.org/licenses/by/4.0/
• 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 robotics 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.

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