Robotic origami folding with dynamic motion primitives

Paper folding is one of the most difficult tasks for multi-fingered robot hands because paper is deformable and its stiffness distribution is nonuniform. In this study, we aimed to achieve dexterous paper folding by extracting some dynamic motion primitives. Each primitive contains visual or force information, a physical model of a sheet of paper is used for analyzing its deformation, and a machine learning method is used for predicting its future state. We also propose a strategy for achieving valley folds in a sheet of paper twice in a row. One problem faced in folding paper is that, in the second fold, the crease line of the first fold disturbs the folding accuracy. We propose some new manipulation techniques to solve this problem. Finally, we show some demonstrations of paper folding achieved with a high success rate.