While surgical robots, such as the da Vinci Surgical System, have become prevalent in minimally invasive surgery, they are predominantly used by the human operator to directly teleoperate the tools. This paper aims to analyse the different methods of human robot shared control in the surgical domain. We propose a reinforcement learning algorithm, transverse generative adversarial imitation learning (tGAIL), which is employed to train the robot from the expert’s demonstration and show competitive generalization ability compared to inverse reinforcement learning and conventional GAIL. We then propose a priority-changing shared control method to effectively combine the surgeon and robot’s strengths by dynamically adjusting control priority based on the deviation distance. We show that using this method in a supervision framework boosts the performance of the human operator when completing the peg transfer task. By learning from the expert and collaborating with the human during the task, the intelligent agent can help to reduce operation time by 31.7% and the human input by 60.5% compared to direct teleoperation.

The manipulation of instruments under a microscope suffers from physiological tremor and human errors, which are inevitable in long microsurgery interventions. Robotic systems developed in recent years for microsurgery are expensive and not flexible, as they cannot use standard instruments, and need the surgeon to modify their operative skills and strategies. In this paper, we introduce a modular robotic system for microsurgery enabling the surgeon to operate using conventional instruments. Our system was implemented using a commercial Kinova robot and a dedicated modular end-effector that uses standard microsurgery instruments. An initial teleoperation validation was carried out by eleven participants, who could successfully control the microsurgery tools to perform basic surgical movements. Furthermore, participants performed a simple anastomosis task with the robot and compared it to manual control. The results showed that robotic control is superior to manual control in simple surgical tasks and the converse in complex tasks. Participants preferred the proposed robotic system due to its user-friendliness and effort reduction.

The utilization of shared control in the realm of surgical robotics augments precision and safety by amalgamating human expertise with autonomous assistance. This paper proposes a user-centered shared control framework enabling a robot to learn from expert demonstration, predict operators’ intent and modulate control authority to provide natural assistance when needed. We employ deep inverse reinforcement learning (IRL) to enable the robot to learn path planning from expert demonstrations with fast convergence, subsequently enhancing the policy with a potential field method. The control authority is allocated seamlessly between the human operator and the autonomous agent based on the prediction of operators’ movement from an adaptive filter and fuzzy logic inference. The proposed method is executed using the da Vinci Research Kit (dVRK) robot in a simulation environment, and its effectiveness is assessed through user performance evaluation in a trajectory tracking task. Compared to direct control and simple shared control, the proposed shared control scheme exhibits superior tracking accuracy and trajectory smoothness under external disturbances. Subjective responses underscore users’ perception of the method’s efficacy in enhancing their performance.

Many surgical tasks require three or more tools working together, where a hands-free interface could extend a surgeon's actions to control a third surgical tool. However, most current interfaces do not allow skilled control of grasping critical to robotic manipulation. Here we first present a systematic study to identify efficient and intuitive interaction strategies to control grasping of a surgical tool. A series of experiments were conducted to evaluate six foot pressure-based gestures. Based on the results, three modular novel foot-machine interfaces were developed, which can be integrated with other motion control interfaces. The identified interaction strategies were implemented to control a laparoscopic tool in a surgical simulator, and evaluated in a user study. The results illustrate how naive participants can operate grasping yielding smooth and pick & place operation.

Most conventional training schemes require trainees to perform repetitive operations. In this way, novices usually require a long learning period and lack personalized training assistance. To address the above issues, we propose a new training framework based on fuzzy inference. Firstly, a modified fuzzy C-means (FCM) classifier is utilised to partition the tasks based on human motor behaviours. Operation performance is comprehensively assessed in each subtask by task-based criterion and the corresponding results are fed back to the trainee in real time during the subtask execution. Once the trainee reaches the operational standard of each subtask, he/she can proceed the next one. Distinguished from traditional repetitive training without intervention, the trainee can leverage purposeful modifications and repetitions in a closed-loop manner. The expertise degree of trainees can lead to differences in training time, avoiding unnecessarily long training sessions for experienced trainees. The proposed method is therefore suitable for the parallel training with different levels of operation. Comparative experiments on microvascular anastomosis task have demonstrated higher training efficiency of the proposed training strategy.

Supernumerary robotic limbs can act as intelligent prostheses or augment the motion of healthy people to achieve actions which are not possible with only two natural hands. However, as trimanual control is not typical in everyday activities, it is still unknown how different training could influence its acquisition. We conducted an experimental study to evaluate the impact of different forms of trimanual action on training. Two groups of twelve subjects were each trained in virtual reality for five weeks using either a three independent goals task or one dependent goal task. The success of their training was then evaluated by comparing their task performance and motion characteristics between sessions. The results show that subjects dramatically improved their trimanual task performance as a result of training. However, while they showed improved motion efficiency and reduced workload for tasks with multiple independent goals with practice, no such improvement was observed when they trained with the one coordinated goal task.

Many teleoperation tasks require three or more tools working together, which need the cooperation of multiple operators. The effectiveness of such schemes may be limited by communication issues between individuals. Trimanipulation by a single operator using an artificial third arm controlled together with their natural arms may address this issue. Foot-controlled interfaces have previously shown the capability to be used for the continuous control of robot arms. However, the use of such interfaces for controlling a supernumerary robotic limb in coordination with the natural limbs is not well understood. In this paper, a teleoperation task imitating physically-coupled hands in a virtual reality scene was conducted with 14 subjects to evaluate human performance during trimanipulation. The participants were required to move three limbs together in a coordinated way mimicking three arms holding a shared physical object. It was found that after a short practice session, three-hand trimanipulation with a single subject’s hands and foot was still slower than dyad operation. However, they displayed similar performance in their success rate and higher motion efficiency than two people cooperating.