Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control
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 if 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 as part of the grand challenge of automating the surgical procedure as a whole. Two AI learning based model predictive control algorithms using vision strategies are proposed and studies including: (1) reinforcement learning and (2) learning from demonstration. The results associated with comparing the performance while utilizing these two 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 grand effort to automate subtask in surgery via a surgical robotic system. It is part of a profound vision in which the role of a surgeon will be redefine as a pure decision maker whereas the vast majority of the manipulation will be conducted autonomously by a surgical robotic system supervised by a surgeon.
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[
CP68] Changyeob Shin , Peter Walker Ferguson1, Sahba Aghajani Pedram1, Ji Ma1,
Erik P. Dutson , , and Jacob Rosen1, Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control, IEEE International Conference on Robotics and Automation (ICRA) 2019, Toronto Canada, May 2019
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