Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control
This work addresses the challenge of automating soft tissue manipulation in surgery, potentially redefining surgeons' roles, but it appears incremental as it builds on existing learning-based control methods.
The study tackled autonomous tissue manipulation in surgery by proposing two AI-based model predictive control algorithms, with the learning from demonstration method showing improved performance in simulation and feasibility on a surgical robot.
Tissue manipulation is a frequently used fundamental subtask of any surgical procedures, and in some cases it may require the involvement of a surgeon's assistant. The complex dynamics of soft tissue as an unstructured environment is one of the main challenges in any attempt to automate the manipulation of it via a surgical robotic system. Two AI learning based model predictive control algorithms using vision strategies are proposed and studied: (1) reinforcement learning and (2) learning from demonstration. Comparison of the performance of these AI algorithms in a simulation setting indicated that the learning from demonstration algorithm can boost the learning policy by initializing the predicted dynamics with given demonstrations. Furthermore, the learning from demonstration algorithm is implemented on a Raven IV surgical robotic system and successfully demonstrated feasibility of the proposed algorithm using an experimental approach. This study is part of a profound vision in which the role of a surgeon will be redefined as a pure decision maker whereas the vast majority of the manipulation will be conducted autonomously by a surgical robotic system. A supplementary video can be found at: http://bionics.seas.ucla.edu/research/surgeryproject17.html