###### Abstract
Civilian applications are equally expansive, ranging from advanced medical surgeries performed by specialists controlling robotic arms, to autonomous robots being remotely controlled for performing search-and-rescue (SAR) operations in disaster-stricken areas [2]. Telerobotics can also benefit industries such as agriculture and construction, with remote-controlled machinery optimizing crop management and robots performing dangerous tasks to minimize human risk and expand operational capabilities. The rise in Virtual Reality (VR) technologies has also increased the performance of teleoperation in many domains, such as remote education [3] and nuclear scenarios [4], but where latency management is especially important is in avoiding avoid VR sickness [5]. Finally, regardless of the application domain, teleoperation will be needed even when the autonomy level of robotics increases, either by providing human demonstrations for Machine Learning (ML) algorithms (e.g., [6]) or as a fallback when autonomous systems fail, even for autonomous driving [7]. To achieve this goal, telerobotics connects a robotic platform on one side to an operator on the other side through a network interconnect with varying complexity, performance, and scale. To provide direct user control, the robot is programmed to follow the motions of the primary device. The robotic platform, often equipped with sensors, actuators, and cameras for feedback, receives commands from the operator and transmits data back to the operator through a network connection. These network connections for telerobotics can range from simple local networks to intricate global networks. These networks comprise a variety of hardware and software components and may implement different access controls, such as virtual private networks (VPNs). These additional control schemes may add additional network routes for security and can affect the overall latency of robotic control [8]. Some telerobotics systems provide force feedback as a sensor data stream, such that the user device not only measures motions but also displays forces to the user--via informational displays or haptic controller feedback. The user interface becomes fully bidirectional, with such telerobotic systems often referred to as being bilateral. Both motion and force may become the input or output to/from the user. This bilateral nature makes control particularly challenging: with multiple feedback loops and even without environmental contact or user intervention, the two devices form an internal closed loop, and communication delays raise significant challenges concerning the stability of the system [9; 10]. The overall communications sent between the operator and robot in such bilateral telerobotic environments consist of a control input from the operator to the robot and feedback from the robot to the operator. The operator initiates communication by using a designated control system, which can be a specialty control system or a simple joystick controller. When an input is received on the control system, the corresponding action is forwarded to the robot. Depending on the system configuration, processing and solving the specific movements, such as joint positions in a robotic arm, may be completed on either the operator or robot side. In most cases, a separate visual feedback loop exists for the operator, often implemented as a camera stream. This visual feedback provides the operator with knowledge of the robot's position and objects of interest. However, high-resolution video or pictures require a much larger amount of data than the data volume required for control data. Consequently, this feedback loop may take considerably longer than the commands being sent. Because of this, the robot may move more than the controller intended. Ultimately, reliable and safe telerobotics thus requires that the latency for both the control and feedback loops are minimized to the greatest extent possible in order to provide a near-presence operator experience.