Learning Medical Suturing Primitives for Autonomous Suturing

This paper focuses on a learning from demonstration approach for autonomous medical suturing. A conditional neural network is used to learn and generate suturing primitives trajectories which were conditioned on desired context points. Using our designed GUI a user could plan and select suturing insertion points. Given the insertion point our model generates joint trajectories on real time satisfying this condition. The generated trajectories combined with a kinematic feedback loop were used to drive an 11-DOF robotic system and shows satisfying abilities to learn and perform suturing primitives autonomously having only a few demonstrations of the movements.