# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 3 Results and Discussion
### Robot Control and Planning  
This category refers to the area that is focused on connecting human input to robot movement to allow for successful teleoperation. Ref. [32] defines three mapping models to categorize teleoperation interfaces: direct, cyber-physical, and homunculus. A simplified representation of these models are shown in Figure 3. These categories help explain the different amounts of information being made between the robotic space and the user space for robot teleoperation. Direct maps the user's hand and eyes directly to the robot. Cyber-physical contains a mapping between the user and the robot by mapping the user's space to a virtual robot and environment, the robot space is similarly mapped back to this virtual space. Finally, the homunculus model combines the last two by decoupling mapping from the direct model, by using a virtual space between the user and the robot, but not requiring a complete virtual robot and environment that matches the real one like in the cyber-physical model. Ref. [33] develops a solution that follows the direct model by imitating a user's upper body pose to teleoperate a humanoid robot. Their method differs from other works in this field in that their system imitates the full human arm pose in the teleoperation rather than just controlling the end-effector pose of the robot. In a user study, they found that their imitation teleoperation method was preferred with users and allowed them to complete the task faster and perceive less overall workload compared to a direct manipulation programming of a robot. Instead of directly tracking  
Figure 3: Simplified versions of the robotic teleoperation mapping models from [32]. an operator's arm pose, refs. [34; 35] both use machine learning techniques to learn a model that will map user input to robot motion in VR teleoperation interfaces. This way the only input required from the operator is the desired end-effector pose, which a handheld VR controller can supply, but still provide efficient teleoperation. Ref. [36] also uses machine learning to improve teleoperation, but their focus is on a predict-then-blend framework to aid the operator in dual-arm robot manipulation tasks. Using their learned model, they predict the manipulation target given the current robot trajectory and then estimate the desired robot motion given the current operator input. This estimate can then be used to correct the input from the operator to improve the efficiency of the teleoperation. Ref. [37] presents a system architecture and controller that falls under the cyber-physical model by utilizing a digital twin of the robot. The idea here is that there are three control loops, one for the operator input, one for the physical robot, and one that serves as the interaction between the digital twin and the actual robot. The benefit of this method is that a robot and human do not need to be co-located and instead the robot can be controlled over long distances. Ref. [38] develops an interaction method that follows the homunculus model by decoupling the human input from the control loop of the robot and avoiding directly using the tracked operator movement in the robot controller. To accomplish this, they design a force controller with two interaction modes, one for coarse movement and one for fine movement. In the coarse movement mode, the operator is able to lock the robot movement and adjust the desired position and orientation of the robot by manipulating a virtual sphere with a coordinate frame. Once satisfied, the operator can then switch to the fine movement mode where orientation is locked and only position is controlled and the robot continuously moves as long as the virtual sphere is being interacted with.