# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Traded Control  
In traded control (introduced by [PERSON] et al., in [359] and [PERSON] et al., in [302], the follower robot sometimes operates autonomously (performing for instance a task of bone drilling [360] or surgical knot tying [361]) and, sometimes, teleoperatively to facilitate the trade-off between teleoperated and autonomous modes. In practice, it can be considered as an intermediary remote control strategy between supervisory or shared control/bilateral control.The surgeon can use automation features when they are efficient, and can take back the full control of the follower robot if necessary. In 2018, [PERSON] et al., highlighted that \"automating ubiquitous surgical subtasks such as suturing makes surgery more efficient, e.g., reduction of surgeon fatigue and/or surgery time\", but \"conducting fully-autonomous surgical task remains risky due to individual difference in human body\" [362]. What differentiates this category from shared control is that the level of autonomy is discretely switched in time on demand of the surgeon. In shared control,the autonomy level is continuously moderated, manually, or automatically. As declared in [313], this collaboration strategy is not commonly used in the literature. A search in July 2020 on \"\(x\) control\" AND teleoperation with \(x\in\{\)bilateral, shared, traded, supervisory\(\}\) in Scopus (See [[https://www.scopus.com](https://www.scopus.com)]([https://www.scopus.com](https://www.scopus.com))) database provided respectively 432, 198, 14, and 104 results, respectively. Moreover, Scopus outputs only one paper corresponding to traded control in a medical context: [313]. The same requests on the IEEE database (See [[https://ieeexplore.ieee.org](https://ieeexplore.ieee.org)]([https://ieeexplore.ieee.org](https://ieeexplore.ieee.org))) provided only seven results for traded control and only two in the medical context, from the same authors as [313]. This can show great potential for investigation on the context of traded control.  
It should be noted that [PERSON] et al. [363] introduced a four-stage model of human information processing in teleoperation tasks (i.e., (1) information acquisition, (2) information analysis, (3) decision and action selection, and (4) action implementation) where the functions in each stage can be automated. For instance, automation at stage 1 can be performed by providing active sensors directing themselves towards the area of interest or by providing adaptive noise filtering. In stage 2, one can envisage predictive functions to bring up more useful information. In stage 3, an expert system may help the surgeon choose the best strategy, and in stage 4, the computer can relieve the surgeon by realizing itself a repetitive or dangerous task (automatic palpation for instance). This model is aimed at guiding roboticists in their design of teleoperation architectures. The multilateral manipulation software framework (MMSF) was introduced in [313], aimed at structuring collaborative tasks to facilitate rapid development of human-robot collaboration models. This framework is to be used upon low-level robotic software frameworks such as ROS. It has been applied for the segmentation of a relatively stiff tissue (e.g., a tumor) from the surrounding soft tissue task.  
Traded control is also useful for tele rehabilitation purposes. In this regard, the concept of learning from demonstration is utilized for robotic rehabilitation [364; 365; 366]. This strategy encompasses two distinct phases: during the first (therapist-in-loop) phase, the therapist interacts directly with a patient through bilateral teleoperation. During the second (therapist-out-of-loop) stage, the follower robot displays the learned therapeutic behavior to the patient (for example via an impedance control loop).