Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control

Abstract

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

Accepted to ICRA 2019

Cited by in corpus (13)

• Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey
• Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit
• Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery
• Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
• Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks
• Sim-to-Real Surgical Robot Learning and Autonomous Planning for Internal Tissue Points Manipulation using Reinforcement Learning
• Automating Surgical Peg Transfer: Calibration with Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans
• Lattice-based shape tracking and servoing of elastic objects
• Cooperative Assistance in Robotic Surgery through Multi-Agent Reinforcement Learning
• Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery
• Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty
• Deformable Object Manipulation With Constraints Using Path Set Planning and Tracking
• Modal-graph 3D shape servoing of deformable objects with raw point clouds