## 2 State of the Art  
The modeling of tasks in simulators has been approached from many different perspectives in the literature. Some works, such as [18; 19; 20; 21], describe VR systems for the simulation of product assembly. They focus on the methods and algorithms related to modeling the physics to assemble components of a product but not to modeling the assembly process itself (i.e., the tasks and subtasks composing the process). Nowadays, there are many different engines, such as Unity 3D, that already incorporate most of these capabilities, so the concern shifts from modeling the physics of the system to modeling the industrial processes themselves. A framework called VR_MATE is presented in [22]. This framework is used for the analysis of maintainability and assembly, including the definition of the structure of the process. [PERSON] et al. [23] present a framework for displaying technical information on AR to replace printed piping and instrumentation diagrams. They use Unity3D and Vuforia to place clickable hotspots over an augmented diagram. [PERSON] [24] presents a framework for large-scale manufacturing layout modeling and a material flow simulation based on the stochastic Petri net model to check reachability in the manufacturing chain and queuing theory for the simulation. This framework allows the creation of AR labels for manufacturing devices and their relationship within the manufacturing chain in real-time, but is specific for manufacturing facilities. A system to automatically generate a sequence of tasks to disassemble a product using its geometry is presented in [25; 26]. Similar systems are used in [14; 15] to guide a worker using a generated task sequence using AR. A framework focused on maintenance, assembly, and disassembly operations with automatic sequence planning is presented in [27]. Even if task sequence planning taking into account the geometric restrictions of a product is useful to simplify the definition of a process, it cannot determine other operations necessary to complete an industrial process that are not related to the geometry itself. Our framework can take advantage of an automated task sequence generator such as the ones mentioned before to simplify some steps of the process definition, but it also has the capabilities to model other types of tasks. For example, an important factor to model in an Industry 4.0 process is the simulation of collaborative robots and their interactions with workers. A human-robot collaboration (HRC) VR scenario with simple task modeling is presented in [28], in which a backing film removal and prepreg laying tasks are simulated. The tasks are very specific to the application scenario and, therefore, there is still a need to model more general tasks including more diverse automated systems. [PERSON] et al. [13] present a system for providing maintenance instructions in a robotic industrial use case. In this system, reusable maintenance sequences are generated once to be reused when needed. AR has also been used to simulate industrial systems with automated systems and human workers. [PERSON] et al. [16] present an AR system which aids the user in an HRC assembly environment. An AR system able to communicate the intent of motion of a robot arm is described in [29]. The objective of the system was to study if the user was able to differentiate colliding and non-colliding motions though robot motion planning overlaid onto the real world. [PERSON] et al. [30] present a study where the application focuses on assessing the best way to move a robot using AR. Here, the framework separates all the features related to the AR environment and the interaction, such as calibration, tracking and recognition of hand gestures, and visual aids, from the process definition. With this approach, any improvement in these areas can be applied to the AR application without affecting any previously defined processes. Similarly, a two-phase framework for AR assembly manual applications using AR is proposed in [31].