# Telepresence Social Robotics towards Co-Presence: A Review
## 3 From Telepresence to Co-Presence Design
### Co-Presence Design
#### 3.1.6 Time Delay Mitigation  
The dynamic nature of the communication medium has an impact on the complexity of teleoperated systems. Time delay, jitter, distance, bandwidth constraints, packet loss, or blackout in internet-based solutions can delay or distort interactions. This can affect the synchronicity (rate of message exchange between operator and bystander), compromising the social presence [33] (e.g., degradations in audio and video streams, control streams, or haptic feedback). Traditional methods to mitigate time delay in telerobotics involved user interface design and control theory-based models (e.g., supervisory control or passivity-based teleoperation) and evolved into predictive displays and control [159]. Recent solutions for time delay issues use _time series prediction_ methods to predict the time delay, robot movements, and user intentions (e.g., user's gaze prediction [174]). These new adaptive-based control methods make use of nonlinear statistical models and neural network (NN) or machine learning (ML) techniques. [PERSON] and [PERSON] [175] determined that a time delay affects human operators' performance while teleoperating manipulators. They realized that the person within the control loop of teleoperated systems under time delays used to adopt a _move-and-wait_ strategy to accomplish certain tasks. To address this problem, they proposed supervisory control [176], in which the robot is preprogrammed or programmed online to perform certain subtasks autonomously. By transmitting only high-level commands, there is a data communication reduction, and task time completion improves. Meanwhile, several extensions of supervisory control were developed, including specific languages to chain tasks or predictive displays (i.e., visualization of a phantom robot model that predicts the motion of the real robot) [177; 178]. _Control-based approaches for time delay mitigation in teleoperation systems_ can be clustered into two classes [31; 179]: (1) _predictive control-based methods_ (e.g., a discrete linear quadratic Gaussian (LQG) controller for teleoperation acting on the sampling rate or output feedback control of multiple-input and multiple-output (MIMO) systems) and (2) _passivity-based methods_ that model the master-slave operator systems and unsure stability and performance under time delay variability (e.g., a two-port network, hybrid matrix, impedance matrix, constant time delay, scattering approach, wave variable, scaling, and geometric scattering). _Time series prediction approaches for time delay mitigation in teleoperation systems_ try to compensate for the time delay, observing past intrinsic patterns to predict the future values [31; 156]. They integrate trends, seasonality, and white noise and can be clustered into two types: _statistical methods_ and _neural network (NN) or machine learning (ML) methods_: (1) _Statistical methods_ (e.g., moving average (MA), linear auto-regression (AR), auto-regression + moving average + nonlinear regression + moving average (ARMA), and auto-regression + moving average + nonlinear component (ARIMA) [180]); (2) _NN or AI methods_ (e.g., recurrent neural networks (RNNs) [181; 182], long short-term memory networks (LSTMs) [183; 184], sequence to sequence (Seq2 Seq) [185; 186], and generative adversarial networks (GANs) [187; 188]). Statistical methods have the advantage of not requiring training with data and are simpler to implement. Although times series prediction traditionally relied on statistical approaches, it has difficulties in modeling the entire set of nonstationary signals. Nevertheless, methods like ARIMA can cope with nonstationary signals. Statistical methods are not appropriate for modeling complex tasks, being more suitable for short-term predictions. Neural networks, on the other hand, have an advantage over statistical approaches in that they enable data description without explicit knowledge of its distribution and can model more complex time series data based on past observations.