Scalable Data Collection System and Model Learning for Pneumatic Artificial Muscles

Abstract

Soft robots made of flexible materials such as rubber and elastomers are attractive because they can guarantee safety due to their physical softness. However, their flexibility causes difficulty in computing accurate mathematical models, making them difficult to control. In this study, we aimed to obtain a learning-based prediction model of pneumatic artificial muscles, one kind of soft robot, in order to achieve high-precision control of the robots. We created a scaleable data collection device that collects air pressure, muscle length, and load data, and trained a time-series prediction model using 5 hours of collected data. Furthermore, we verified the effectiveness of the method by executing a control task using the learned prediction model.