Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning.

ABSTRACT: Magnetically manipulated medical robots are a promising alternative to current robotic platforms, allowing for miniaturization and tetherless actuation. Controlling such systems autonomously may enable safe, accurate operation. However, classical control methods require rigorous models of magnetic fields, robot dynamics, and robot environments, which can be difficult to generate. Model-free reinforcement learning (RL) offers an alternative that can bypass these requirements. We apply RL to a robotic magnetic needle manipulation system. Reinforcement learning algorithms often require long runtimes, making them impractical for many surgical robotics applications, most of which require careful, constant monitoring. Our approach first constructs a model-based simulation (MBS) on guided real-wo

SUBMITTER: Barnoy Y 

PROVIDER: S-EPMC10438915 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature