Model-free control for soft manipulators based on reinforcement learning

Most control methods of soft manipulators are developed based on physical models derived from mathematical analysis or learning methods. However, due to internal nonlinearity and external uncertain disturbances, it is difficult to build an accurate model, further, these methods lack robustness and portability among different prototypes. In this work, we propose a model-free control method based on reinforcement learning and implement it on a multi-segment soft manipulator in 2D plane, which focuses on the learning of control strategy rather than the physical model. The control strategy is validated to be effective and robust in prototype experiments, where we design a simulation method to speed up the training process.