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.

自动化的崛起为制造过程的高效性提供了机会，但往往会损害灵活性，无法及时响应市场需求的变化和对定制化的需求。人机协作通过结合机器的强大和精确性以及人类的智慧和感知能力来解决这些挑战。本文概念化和提出了一个基于机器学习的自主机械臂的实施框架，该框架包括人机循环原则，并利用扩展现实（XR）促进人与机器之间的直观交流和编程。此外，概念框架预见到人类直接参与机器学习过程，从而实现更高的适应性和任务通用化。本文突出强调支持所提出框架的关键技术，强调全面发展数字生态系统的重要性。此外，我们回顾了扩展现实在人机协作中的现有实施方法，展示了多样的观点和方法论。讨论了挑战和未来展望，深入探讨了扩展现实在工业领域实现更自然的人机交互和融合的主要障碍和潜在研究方向。

增强人机协作的扩展现实技术：人为循环方法

Extended Reality for Enhanced Human-Robot Collaboration: a  Human-in-the-Loop Approach

The volumetric representation of human interactions is one of the fundamental
domains in the development of immersive media productions and telecommunication
applications. Particularly in the context of the rapid advancement of Extended
Reality (XR) applications, this volumetric data has proven to be an essential
technology for future XR elaboration. In this work, we present a new multimodal
database to help advance the development of immersive technologies. Our
proposed database provides ethically compliant and diverse volumetric data, in
particular 27 participants displaying posed facial expressions and subtle body
movements while speaking, plus 11 participants wearing head-mounted displays
(HMDs). The recording system consists of a volumetric capture (VoCap) studio,
including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In
addition to textured meshes, point clouds, and multi-view RGB-D data, we use
one Lytro Illum camera for providing light field (LF) data simultaneously.
Finally, we also provide an evaluation of our dataset employment with regard to
the tasks of facial expression classification, HMDs removal, and point cloud
reconstruction. The dataset can be helpful in the evaluation and performance
testing of various XR algorithms, including but not limited to facial
expression recognition and reconstruction, facial reenactment, and volumetric
video. HEADSET and its all associated raw data and license agreement will be
publicly available for research purposes.

我们提出了一个新的多模态数据库来帮助推进沉浸技术的发展，该数据库提供了符合道德规范并且多样化的体积数据，可以支持面部表情识别等扩展现实算法的评估和性能测试。