A Step Towards Conditional Autonomy - Robotic Appendectomy

In recent years, Robot-Assisted Minimally Invasive Surgery (RAMIS) has been widely adopted worldwide due to its high precision, improved ergonomics and intuitive control. With the advances in artificial intelligence and surgical robot technologies, it is anticipated that the cognitive load on the surgeons could be relieved with a higher level of autonomy on surgical robots as well as simultaneously improving the precision of surgical robots' operations, minimising the risk of human errors and leading to safer operations. However, much research is still focused on task autonomy due to the complexity of the operations, and the limited clinical data on robotic surgery in some specific scenarios. Therefore, recent research has widely used simulated surgery scenarios to collect data to train artificial intelligence (AI) models. In this paper, we propose a method as a step towards conditional autonomy for robotic appendectomy. To achieve conditional autonomy, demonstration data was collected from human operators performing the appendectomy in a simulated robotic scene. The robotic instrument movements and trajectories for performing the appendectomy were learned from the demonstrated data. Once the surgeon specifies the location of the appendix, the proposed framework can carry out the appendectomy autonomously by using the dynamic motion primitives (DMP) generated from the learned surgical movements. Through the extensive validations in a simulated environment and on the da Vinci Research Kit (dVRK), we have shown that the proposed method can carry out the appendectomy procedure semi-automatically. Based on five metrics, it shows that the framework can reduce the total task path length, completion time and appendix stump length while maintaining a high degree of similarity to the demonstrated trajectory. This work demonstrated the feasibility of conditional autonomy in robotic surgeries.