# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 3 Results and Discussion
### Visualization  
The Visualization category encompasses concepts mainly focused on displaying context to the user. Currently, most of this work is in identifying the best way to represent the real world in VR. Ref. [20] investigates the influence of displaying different levels of environmental information has on task performance and operator situation awareness in VR robot interfaces. Specifically, they look at two informational contexts: full information and a representative model. The full information context shows all the contextual and task-related information to the operator, which includes a mesh of the environment surrounding the robot. While the representative model context shows only the task-related information, which does not include any visualization of the robot's surrounding environment. Ultimately, they find that the time to complete the task is reduced when displaying full information compared to the representative model, but accuracy remains the same between both. However, attentional demand was significantly higher during full information. Ref. [21] saw similar results when comparing a representative model visualization of the full environment to a real-time point cloud visualization of the real environment and found that the success rate and usability ratings were higher with the point cloud visualization. However, point cloud visualization can be computationally expensive and require a large amount of bandwidth. Ref. [22] looks to solve this problem by presenting a method to efficiently process and visualize point clouds for VR applications. Ref. [23] evaluates how using virtual features, such as a 3D robot model, object target poses, or displaying distance to a target, effects operator performance in completing teleoperation pick-and-place tasks. Overall, their results show that virtual features increase the accuracy and efficiency of the task performance and significantly reduce differences between expert and non-expert users. Ref. [24] compares an immersive 3D visualization to a standard 2D video-based visualization. They found that by displaying real-time 3D scene information improves the ability to self-localize in the scene, maneuver around corners, avoid obstacles, assess terrain for navigability, and control the visualization view. Ref. [25] takes a slightly different approach and assesses how different viewpoints can affect success when teleoperating a construction robot. Overall, they show that an active viewpoint, where the user is able to move their head to change the visualization improves the success of the teleoperation compared to an automatic bird's-eye viewpoint that coincides with the movement of the robot to maintain the end-effector in the view. Virtual reality causes motion sickness in some individuals and can be compounded depending on how data from a robot is being presented. Ref. [26] aims to identify the effect of linear velocity, acceleration, and angular displacement on VR motion sickness. They use their findings to develop a head-synchronized controller to reduce motion sickness when controlling a drone with VR. The developed controller reduces angular velocity by synchronizing the drone's movement with the user's head movement. This in turn reduces sensory conflict for the operator, therefore reducing the motion sickness as well. Similarly, ref. [27] analyzes the effects of visual and control latency in drones when using VR. Unsurprisingly, they find that an increase in latency resulted in worse flight performance and a higher level of motion sickness. However, they also find that the more time users spent operating the system, the more tolerant they became. Refs. [28; 29] both present methods to decouple an operator's head movement from the robot's current view, i.e., the robot's camera, when using VR for robot teleoperation. Traditionally, an operator's head movement directly controls the robot's camera.