Investigating exploration for deep reinforcement learning of concentric tube robot control.
Journal:
International journal of computer assisted radiology and surgery
Published Date:
Jun 6, 2020
Abstract
PURPOSE: Concentric tube robots are composed of multiple concentric, pre-curved, super-elastic, telescopic tubes that are compliant and have a small diameter suitable for interventions that must be minimally invasive like fetal surgery. Combinations of rotation and extension of the tubes can alter the robot's shape but the inverse kinematics are complex to model due to the challenge of incorporating friction and other tube interactions or manufacturing imperfections. We propose a model-free reinforcement learning approach to form the inverse kinematics solution and directly obtain a control policy.