# UX in AR-Supported Industrial Human-Robot Collaborative Tasks: A Systematic Review
Based on [11], it could be stated that:  
* The human-centered view is primarily concerned _\"with how a robot can fulfil its task specification in a manner that is acceptable and comfortable to humans\"_;
* The robot-centered view _\"emphasizes the view of a robot as a creature, i.e., an autonomous entity that is pursuing its own goals based on its motivations, drives and emotions, whereby interaction with people serves to fulfil some of its \"needs\"_;
* The robot-cognition view considers _\"the robot as an intelligent system (in a traditional AI sense), i.e., a machine that makes decisions on its own and solves problems it faces as part of the tasks it needs to perform in a particular application domain.\"_. Such considerations are valid even if multiple humans and robots are involved in the interaction. All these terminological and conceptual distinctions demonstrate the intrinsic complexity of a HRI task and the need of a structured approach to appropriately encompass all its peculiarities. However, in practice, collaborative robots (i.e., \"_cobots_\") are currently regulated by the ISO 10218 technical specification document [12], providing a precise interpretation of their roles and natures, and defining the safety requirements for industrial collaborative systems. The main focus is actually for safety as addressed by ISO 15066 [13], neglecting other human implications. We could say that the focus is on the robot-centered view, and only marginally on the robot cognition-centered view where the human-centered aspects are only considered for safety purposes. In this context, the use of AR technologies as task support tools force us to pay attention to the human-centered view by supporting HRI at different levels (i.e., coexistence, cooperation, and collaboration), thanks to the creation of specific, contextual human knowledge on the ongoing process, and the promotion of know-how development and know-to-cooperate abilities [9]. Indeed, an effective AR interface should define their contents according to the level of interaction realized between the operator and the robots, since each level is characterized by a different form of interaction that requires specific features in the AR interface to properly support the tasks. Indeed, AR introduces digitized information into the real working environment to augment the UX, promoting system usability and object visibility, while reducing the operator's physical and cognitive workload. Current AR applications are provided mainly through tablets since head-mounted display are still far from being industrially reliable, especially as for ergonomic aspects. The state of art in literature regarding AR-supported HRI highlighted different application areas: visualizing robot actions or faults to support troubleshooting [14]; controlling robots combining head and eye gaze; visual simultaneous localization and mapping algorithms [15]; understanding the impact of AR cues on human attention [16]; supporting human-robot  
Figure 1: Main AR applications areas according to the three levels of HRI. collaborative assembly [17]; providing workspace and robot's volume monitoring [8; 18]; improving interaction efficiency by reducing the physical strength (especially in heavy-duty industries) or letting older people to continue working in production facilities [19]; and by helping operators to have an immediate comprehension of the robot intentions in a quick and intuitive way (e.g., making visible the robot's planned motion and task state) [20; 21] or adapting the AR contents to the specific environmental or task conditions [22; 23]. However, the majority of existing AR solutions looks at technology and robots, while neglecting the human aspects [10]. The main problem in AR-supported HRI is the lack of user friendly and intuitive interfaces implemented in accordance with the interaction design principles to guide users.