Axis-space framework for cable-driven soft continuum robot control via reinforcement learning

Abstract

Cable-driven soft continuum robots are important tools in minimally invasive surgery (MIS) to reduce the lesions, pain and risk of infection. The feasibility of employing deep reinforcement learning (DRL) for controlling cable-driven continuum robots has been investigated; however, a considerable gap between simulations and the real world exists. Here we introduce a deep reinforcement learning-based method, the Axis-Space (AS) framework, which accelerates the computational speed and improves the accuracy of robotic control by reducing sample complexity (SC) and the number of training steps. In this framework, the SC was reduced through the design of state space and action space. We demonstrate that our framework could control a cable driven soft continuum robot with four tendons per section. Compared with the Double Deep Q-learning Network (DDQN) controller, the proposed controller increased the convergence speed by more than 11-fold, and reduced the positioning error by over 10-fold. This framework provides a robust method for soft robotics control.