# Closed-Loop Robotic Arm Manipulation Based on Mixed Reality
## 1 Introduction  
Modern manufacturing systems working under the Fourth Industrial Revolution (or Industry 4.0) paradigm, are constantly evolving. Industry 4.0 has introduced a wide variety of technologies and techniques for improving both productivity and working conditions in modern manufacturing plants. However, it is of great importance to keep in mind that human resources and, more specifically, shop-floor technicians must retain the engineers' center of attention [1]. With recent technological advances, the integration of robotic manipulation has been leveraged during the last decades, and the path to the factories of the future leads to the design and development of collaborative environments [2]. Therefore, what is needed is the provision of suitable tools that will enable continuous and flawless communication between human operators and machines [3, 4]. Additionally, Human-Robot Interaction (HRI) poses new challenges to the manufacturing landscape, such as safety, autonomy, and social acceptance, as the demand for collaborative robots or cobets [5] to interact, collaborate, and assist human operators grows. Smart manufacturing technologies [6] are gradually displacing jobs that are repetitive, monotonous, and low-skilled. Artificial Intelligence (AI)-based systems have great potential for automating jobs that previously required human intelligence for adaptive decision making [7]. In collaborative manufacturing cells, safety is also a major issue. Robotics and automation are creating new and more skill-demanding job opportunities. This shift has led to the reshaping of manufacturing to make it smarter and safer, not only in terms of production processes, but also in terms of human labor, with new skills and competencies required [8]. Human-RobotCollaboration (HRC) [9] also poses significant challenges, especially in terms of safety [10]. The ability to predict human actions [10; 11] and the capability to plan and continuously replan safe robot trajectories based on predicted/observed human actions [11] have been identified as two major challenges in the literature. As mentioned in the previous paragraph, the current era is characterized by immense technological advances. Concretely, since there is also a great development of the Information and Communication Technologies (ICT), both in terms of hardware and software, several other digital technologies, such as Extended Reality (XR), have become more popular in the industrial world [12]. Extended Reality (XR) is an umbrella term, including Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) [13]. Under the scope of the current research work, special attention will be given to MR. MR is similar to AR, since it partially immerses the users, i.e., it involves the registration of digital content on their field of view (FoV). However, the main difference with AR is that MR enables the interaction of the user with the digital contents, i.e., the holograms. The capabilities of the above-mentioned technologies are leveraged by the fact that Artificial Intelligence (AI) technologies have become mainstream [14]. As a result of the above-mentioned transition and progress, HRC is one of the outcomes that allows humans and robots to collaborate to achieve common goals. As a result, new HRI methods encourage collaboration, especially in more complex scenarios. Safety is a critical consideration in the design and implementation of any new technology that aims to work in close collaboration with operators during the age of industrialization and automation [15], particularly as the human-centric industrial revolution or Industry 5.0 approaches [16]. In the research work of [PERSON] et al. [17], safety risks in HRCs are identified mainly in the field of collaborative assembly stations, in which non-intentional contact between humans and robots is one of the main safety risks. Similarly, in [18] the authors have investigated the available literature in an attempt to highlight challenges in HRC implementation.