  The development of human-robot collaboration has the ability to improve
manufacturing system performance by leveraging the unique strengths of both
humans and robots. On the shop floor, human operators contribute with their
adaptability and flexibility in dynamic situations, while robots provide
precision and the ability to perform repetitive tasks. However, the
communication gap between human operators and robots limits the collaboration
and coordination of human-robot teams in manufacturing systems. Our research
presents a human-robot collaborative assembly framework that utilizes a large
language model for enhancing communication in manufacturing environments. The
framework facilitates human-robot communication by integrating voice commands
through natural language for task management. A case study for an assembly task
demonstrates the framework's ability to process natural language inputs and
address real-time assembly challenges, emphasizing adaptability to language
variation and efficiency in error resolution. The results suggest that large
language models have the potential to improve human-robot interaction for
collaborative manufacturing assembly applications.
