  Learning from demonstration is one of the most promising methods to
counteract the challenging long-term trends in repetitive industrial assembly.
It offers not only a programming technique that is accessible to workers on the
shop floor, reducing the need for robot experts and the associated costs but
also a possible solution to the observable shift from mass-production to
mass-customisation through flexible and generalising systems. Since the
emergence of the learning from demonstration idea in the 1980s, its
methodologies, capabilities, and achievements have constantly evolved. However,
despite reports of continued progress in academic publications, the concept has
not yet robustly emerged across the assembly industry. In light of its great
potential, this paper presents the findings from a systematic literature review
following the updated Preferred Reporting Items for Systematic Reviews (PRISMA)
guidelines. It aims to provide an overview of the state-of-the-art learning
from demonstration solutions developed for assembly-related tasks and offer a
critical discussion of remaining obstacles in order to drive its progression
towards meaningful deployments. The analysis includes a total of 61 papers over
the period of 2013-2023 sourced from Scopus and Web of Science databases.
Findings indicate that learning from demonstration has attained a significant
level of maturity within the research environment, as evidenced by thorough
experimental achievements, proving its great promise for industrial assembly
applications. However, critical obstacles exist in the area of proven
practicability, task complexity and diversity, generalisation, performance
evaluation and integration concepts that require attention to promote its
widespread adoption and create a seamless transition into industrial practices.
