## 5 Applications in Human-Robot Interaction
### Human-Robot Collaboration
A framework denoted as Operator Support Module (OSM) was presented in [46] for facilitating collaboration by providing cognitive support to operators using AR technologies, voice commands, and gestures. The authors stated that OSM can recognize objects and provide feedback to users, e.g., when a wrong object is manipulated or when a step in the assembly task is completed. Due to the expensive deep learning methods used in [46], the system was distributed on different computers. A module in Unity denoted OSM Controller was proposed to handle communication between the different elements in the system. This module connects information generated by Unity with ROS (using rosbridge) and Unity with wearable and smart devices (using Mirror). We observe that studying ergonomic and performance aspects in HRC industrial tasks using XR systems is becoming an emergent topic. In this context, ref. [89] proposed a methodology to determine the accuracy and quality of a human motion capture system integrating an inertial measurement unit (IMU) and Vive-based HTC during physical human-robot interaction. For this, they created a digital twin of the actual system in which human and robot motions are replicated in real-time in the visual environment. To transfer the actual robot motion to the digital robot model, they used ROS#. An effort to understand the variables affecting the trust involved in HRC using AR technologies was presented in [83]. The presented results suggest that context awareness and human safety are key variables affecting trust in HRC scenarios. Though presented in [83] was performed with a Turtlebot mobile robot, the authors stated that similar results could be obtained with industrial cobots, which are heavy and strong. However, their authors note that further research involving a real industrial cobot is needed in order to better understand the factors affecting trust in cobots. Most recently, ref. [90] proposed a software suite for supporting the operator's interaction with mobile and industrial robot co-workers. This suite proposes a solution for easy programming, supporting autonomous operations, assisting operators in assembly processes, and enhancing safety awareness and resilience of production processes. The experimental evaluation proposed in [90] suggests that the proposed system can reduce the time required by operators to recover from operational failures and safety violations. AR for Robots Collaborating with a Human (ARROCH) [86] is a system enabling communication between humans and multiple robots. With ARROCH, an operator can use a tablet to visualize the robots' current states and planned actions (intentions). Finally, the SIGverse [37] cloud-based VR platform, built on top of Unity, proposes solutions for performing different HRI experiments. With SIGverse, one or more users can simultaneously interact with virtual robots at the same time using the Photon Unity Networking (PUN) asset for Unity, which provides multiplayer networking features. A bridging mechanism is used to ensure communication between VR scenes created in Unity and ROS modules. This bridging mechanism uses WebSockets and the JSON format to send small-size data from Unity to ROS, a solution based on TCP/IP sockets, and uses binary JavaScript object notation (BSON) format to send large data messages. The user cases reported in [37] include robotic competitions, collaborative clean-up of a room using human pointing gestures, evaluation of the subjective quality of HRI, and motion learning by demonstration. SIGVerse is available on GitHub ([[https://github.com/SIGVerse](https://github.com/SIGVerse)]([https://github.com/SIGVerse](https://github.com/SIGVerse)), accessed on 10 October 2022), and its documentation can be found on its website ([[http://www.sigverse.org/wiki/en/](http://www.sigverse.org/wiki/en/)]([http://www.sigverse.org/wiki/en/](http://www.sigverse.org/wiki/en/)), accessed on 10 October 2022).