  The industry of the future, also known as Industry 5.0, aims to modernize
production tools, digitize workshops, and cultivate the invaluable human
capital within the company. Industry 5.0 can't be done without fostering a
workforce that is not only technologically adept but also has enhanced skills
and knowledge. Specifically, collaborative robotics plays a key role in
automating strenuous or repetitive tasks, enabling human cognitive functions to
contribute to quality and innovation. In manual manufacturing, however, some of
these tasks remain challenging to automate without sacrificing quality. In
certain situations, these tasks require operators to dynamically organize their
mental, perceptual, and gestural activities. In other words, skills that are
not yet adequately explained and digitally modeled to allow a machine in an
industrial context to reproduce them, even in an approximate manner. Some tasks
in welding serve as a perfect example. Drawing from the knowledge of cognitive
and developmental psychology, professional didactics, and collaborative
robotics research, our work aims to find a way to digitally model manual
manufacturing skills to enhance the automation of tasks that are still
challenging to robotize. Using welding as an example, we seek to develop, test,
and deploy a methodology transferable to other domains. The purpose of this
article is to present the experimental setup used to achieve these objectives.
