Deep Reinforcement Learning for Concentric Tube Robot Path Following

TL;DR

This paper introduces a deep reinforcement learning method for controlling Concentric Tube Robots, demonstrating improved robustness and generalization across multiple systems, with promising simulation results and domain transfer strategies for future hardware deployment.

Contribution

It presents the first deep reinforcement learning control approach that generalizes across multiple CTR systems and explores domain transfer techniques for real-world application.

Findings

RL approach outperforms classical methods in simulation

Generalizes across 2-4 CTR systems

Domain randomization aids in sim-to-real transfer

Abstract

As surgical interventions trend towards minimally invasive approaches, Concentric Tube Robots (CTRs) have been explored for various interventions such as brain, eye, fetoscopic, lung, cardiac and prostate surgeries. Arranged concentrically, each tube is rotated and translated independently to move the robot end-effector position, making kinematics and control challenging. Classical model-based approaches have been previously investigated with developments in deep learning based approaches outperforming more classical approaches in both forward kinematics and shape estimation. We propose a deep reinforcement learning approach to control where we generalise across two to four systems, an element not yet achieved in any other deep learning approach for CTRs. In this way we explore the likely robustness of the control approach. Also investigated is the impact of rotational constraints applied on tube actuation and the effects on error metrics. We evaluate inverse kinematics errors and tracking error for path following tasks and compare the results to those achieved using state of the art methods. Additionally, as current results are performed in simulation, we also investigate a domain transfer approach known as domain randomization and evaluate error metrics as an initial step towards hardware implementation. Finally, we compare our method to a Jacobian approach found in literature.