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It is challenging to control a soft robot, where reinforcement learningmethods have been applied with promising results. However, due to the poorsample efficiency, reinforcement learning methods require a large collection oftraining data, which limits their applications. In this paper, we propose aQ-learning controller for a physical soft robot, in which pre-trained modelsusing data from a rough simulator are applied to improve the performance of thecontroller. We implement the method on our soft robot, i.e., HoneycombPneumatic Network (HPN) arm. The experiments show that the usage of pre-trainedmodels can not only reduce the amount of the real-world training data, but alsogreatly improve its accuracy and convergence rate.

A Q-learning Control Method for a Soft Robotic Arm Utilizing Training Data from a Rough Simulator