  The rise of automation has provided an opportunity to achieve higher
efficiency in manufacturing processes, yet it often compromises the flexibility
required to promptly respond to evolving market needs and meet the demand for
customization. Human-robot collaboration attempts to tackle these challenges by
combining the strength and precision of machines with human ingenuity and
perceptual understanding. In this paper, we conceptualize and propose an
implementation framework for an autonomous, machine learning-based manipulator
that incorporates human-in-the-loop principles and leverages Extended Reality
(XR) to facilitate intuitive communication and programming between humans and
robots. Furthermore, the conceptual framework foresees human involvement
directly in the robot learning process, resulting in higher adaptability and
task generalization. The paper highlights key technologies enabling the
proposed framework, emphasizing the importance of developing the digital
ecosystem as a whole. Additionally, we review the existent implementation
approaches of XR in human-robot collaboration, showcasing diverse perspectives
and methodologies. The challenges and future outlooks are discussed, delving
into the major obstacles and potential research avenues of XR for more natural
human-robot interaction and integration in the industrial landscape.
