# UX in AR-Supported Industrial Human-Robot Collaborative Tasks: A Systematic Review
## 3 Review Results
### What Are the State of the Art UX Approaches in AR-Supported Collaborative Solutions?
[48], a collaborative task concerning playing board games was explored and evaluated by examining various interaction opportunities arising when humans and robots collaborate. This interesting analysis was related to two contrasting robotic behavioral conditions which have been tested: a human-centric condition where robot behavior is more accustomed to human obedience, and a robot-centric one where suggestions coming from the operator are neglected. Statistical results on the final user testing phase related to a custom questionnaire allows for a reinforced idea of the centrality of a human-centric condition to increase the sense of collaboration of O4.0. Moreover, [PERSON] et al. [56] stressed that safety is deemed as one of the most relevant aspects in human-robot collaborative systems and context-awareness information is unavoidably important to enhance user perception. Analogously, [PERSON] et al. [57] proposed two separate approaches to draw AR paths, respectively, a free space and a surface trajectory one. Such proposals could be effectively integrated to optimize robot's programming phases with a UX sensitivity, reducing programming time, and allowing the worker to selectively inspect different robot trajectories and to work on them in a user-friendly interface. For an optimal collaboration, robot intention is another source of essential information within a HCD approach: a general indifference on the topic emerges from actual selection, although [PERSON] et al. [58] described a temporal and-or graph (T-AOG) to allow the human understanding of the robot's internal decision-making process, to supervise its action planner, or to monitor its latent states (i.e., forces and moments exerted while interacting). As emerged from the review findings, several benefits derived from a UX-based approach when implementing AR-supported collaborative tasks, both objective and subjective: a systematic cognitive and physical relief on the operator, an increased working efficiency, a reduction in operational time and sense of frustration when interacting with shop floor interfaces, and an improved sense of safety and inclusiveness while collaborating with the robotic counterpart. Such conclusions were mainly reported after a user testing campaign in which standard or customized questionnaires were designated to collect final tester impressions to be subsequently reanalyzed by the papers' authors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Benefits** & **Adopted UX Tools** & **Area of Application** \\ \hline [PERSON], [PERSON], and [PERSON] [45] & End-user’s intentions understanding to reduce operator cognitive burden & Custom questionnaire & Object manipulation \\ \hline [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [50] & The system’s final application must be considered to prevent wrong choices in terms of interfaces and to avoid physical and cognitive repercussion on the user & NASA-TLX & Large-scale, labor-intensive manufacturing tasks \\ \hline [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [57] & Reducing robots’ programming operation time and cognitive demand & Custom questionnaire & Robot programming \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Papers focusing on added value related to adoption of UX approaches in HRI.