Large Language Models for Industrial Robots: Applications, Challenges, and Future Directions

With the accelerating intelligent transformation of global manufacturing, industrial robots have become critical equipment in modern smart factories. However, the growing prevalence of multi-variety, small-batch production has made the high programming threshold, tedious reconfiguration, and limited environmental adaptability of traditional industrial robots increasingly prominent. Over the past few years, large language models (LLMs) have proven to have strong semantic comprehension, knowledge reasoning, and task planning abilities, a new avenue in the intelligent upgrading of industrial robots. The systematic review of the literature on the topic of the industrial robot driven by LLM is presented in this paper, including the mainstream technical directions, and the representative studies in five areas of application: industrial inspection, assembly and collaboration, maintenance and fault recovery, manufacturing operation planning, and robot navigation. Based on this, the paper examines the key challenges to be addressed at present, including model output reliability and safety, the simulation-to-reality gap and real-time constraints, and data scarcity and distribution shift. It also discusses future research opportunities, including knowledge enrichment and reliable reasoning, lightweight deployment and edge computing, and life-long learning and domain adaptation. The purpose of this paper is to serve as a reference in future studies and implementation of LLMs in industrial robots.