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.

翻译：自动化的兴起为制造过程实现更高效率提供了机遇，然而其往往牺牲了快速响应不断变化的市场需求及满足定制化所需的灵活性。人机协作试图通过结合机器的力量与精度以及人类的创造力与感知理解来应对这些挑战。本文提出并概念化了一种基于机器学习的自主机械臂实现框架，该框架融入人在回路原则，并利用扩展现实（XR）技术促进人与机器人之间的直观通信与编程。此外，该概念框架预见人类直接参与机器人学习过程，从而实现更高的适应性与任务泛化能力。本文重点阐述了支撑该框架的关键技术，强调发展整体数字生态系统的重要性。同时，我们综述了现有XR在人机协作中的实现方法，展示了多元化的视角与方法论。文中进一步探讨了面临的挑战与未来展望，深入分析了XR在工业领域实现更自然人机交互与融合的主要障碍及潜在研究方向。