Abstract
AbstractCable-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.
Funder
National Science Foundation of China | National Natural Science Foundation of China-Yunnan Joint Fund
Natural Science Foundation of Guangdong Province
Shenzhen Science and Technology Innovation Commission
Publisher
Springer Science and Business Media LLC