Human-Machine Collaborative surgery using learned models

In the future of surgery, tele-operated robotic assistants will offer the possibility of performing certain commonly occurring tasks autonomously. Using a natural division of tasks into subtasks, we propose a novel surgical Human-Machine Collaborative (HMC) system in which portions of a surgical task are performed autonomously under complete surgeon's control, and other portions manually. Our system automatically identifies the completion of a manual subtask, seamlessly executes the next automated task, and then returns control back to the surgeon. Our approach is based on learning from demonstration. It uses Hidden Markov Models for the recognition of task completion and temporal curve averaging for learning the executed motions. We demonstrate our approach using a da Vinci tele-surgical robot. We show on two illustrative tasks where such human-machine collaboration is intuitive that automated control improves the usage of the master manipulator workspace. Because such a system does not limit the traditional use of the robot, but merely enhances its capabilities while leaving full control to the surgeon, it provides a safe and acceptable solution for surgical performance enhancement.

Authors

• Nicolas Padoy
• Gregory D. Hager

Keywords

• Hidden Markov models
• Instruments
• Trajectory
• Robots
• Needles
• Surgery
• Manuals
• Human-machine Collaboration
• Surgeons
• Hidden Markov Model
• Use Of Robots
• Collaborative System
• Inverse Reinforcement Learning
• Surgical Tasks
• Coordinate System
• Endoscopic
• Cognitive Load
• Transition Probabilities
• Automata
• Path Planning
• Recognition System
• Suturing
• Large Motion
• Needle Insertion
• Manual Operation
• Time Warping
• Dynamic Time Warping
• Da Vinci Surgical System
• Seamless Transition
• Average Trajectory
• Trajectory Control
• Camera Coordinate System