# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Shared Control
#### 3.2.3 Shared Control Synthesis  
Table 1 synthesizes a wide range of works in the literature. This table does not include all existing works in the literature due to the significant size of existing research. However, it provides examples of each category. The table focuses on medical application, the level of the task performed by the assistance, the activity of the assistance, the presence of haptic feedback, the corresponding topology, the autonomy category, and the solutions for shared control. This table shows that solutions are provided in the literature from low to high levels of control layers, for a wide variety of applications. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Papers** & **Application** & **Task Level** & **Assistance** & **Haptic Feedback** & **Topology** & **Category** & **Solution** \\ \hline
[313] & Surgical Manipulation (palation) & High-level & Decision Making & ✓ & & & Dimension Reduction \\ \hline
[327] & Surgical Manipulation (dissection) & & & ✗ & & & Virtual Fistures \\ \hline
[326] & Surgical Manipulation (tracking) & & & ✗ & SL/SF & Task Decomposition & \\ \hline
[328] & Surgical Manipulation (tissue contact) & Low-level & Motion Control & & & & Motion Compensation \\ \hline
[16] & Tele-echography & & & ✓ & & & \\ \hline
[348] & Surgical Grasping & & & & & & \\ \hline
[345] & Surgical Cutting & & & & & Authority Blending & Linear Blending \\ \hline
[325,340] & & High-level & Decision Making & ✗ & & & \\ \hline
[247] & Assistive Rehabilitation & & & & & Task Decomposition & Virtual Fistures \\ \hline
[349] & & & & & & & Nonlinear Blending \\ \hline
[206,208,268,269, 308,343,344,347, 351] & & Low-level & Motion Control & ✓ & & & spring-damper) \\ \hline
[352] & Surgical Training & & & & & & \\ \hline
[218,324] & & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Shared control samples for teletherapy applications. Besides medical applications, there exist several other works that relate to shared control strategies for other applications. A few of them are cited below, selected for their interest. For instance, in [353], authors have developed virtual fixtures to help operators grasp objects in a scene, resulting in an improvement of 20.8%, 20.1%, 32.5% in terms of completion time, linear trajectory, and perceived effectiveness, respectively, between the proposed approach and standard teleoperation. As surgical grasping is a common task, such improvement should help enhance teletherapy applications.At a higher control level, [PERSON] et al., highlighted that the assistance provided by the autonomous system requires \"knowing\" the user's goal to be effective. Solutions found in the literature based on a predict-then-act model (see [341] for corresponding references) may not be effective due to the intrinsic limitations of prior prediction of human intentions. The authors then proposed a real-time observation based on a Markov decision process with online optimization to determine the most probable current goal of the user. Experiments showed that this approach _\"reduced the task time compared to predict-then-act, required less user input, decreased user idling time, and resulted in fewer user-robot collisions\"_. Knowing the high cognitive requirement of surgical operations, such an approach should also be interesting in a teletherapy context. In [354], the assistance is achieved through programming by demonstration (PbD). The assistance and user inputs are blended according to two confidence indices (on user and assistance) computed online that determine their relative weight. The assistive system uses a Gaussian mixture model (GMM) to represent the task, and the desired state associated with a confidence level is obtained using a Gaussian mixture regression (GMR). Experimental results on teleoperated object manipulations showed a light preference for the trained solution. Authors conclude that this is a promising approach that still requires investigation to become more effective. Other similar shared autonomy formulations can be found in [355; 356].