Learning-Based Fabric Folding and Box Wrapping

Abstract

Manipulation of deformable objects is an essential task in surgery, the textile industry, and household tasks, such as washing, hanging, and folding clothes. However, current studies on fabric manipulation, have rarely considered the scenes in which rigid objects have to be wrapped by fabrics. This type of operation is widely adopted in the logistic packaging and packaging of surgical instrument baskets. In this study, we propose a method to perform this operation, which can be used in fabric folding or wrapping a box with fabric. Owing to the complex dynamics and configuration spaces of fabric, our method is based on deep imitation learning to estimate pick-place points and the phase of the manipulation process. The dataset was completely generated from an open-source physical simulator. To make the data as realistic as that in actual scenarios, we adopted domain randomization and rendered the texture of fabric and box from the real world to simulation data. This not only helps in transferring the learned policies to a physical robot, but also allows the robot to wrap a box with complex patterns. The experiments demonstrate the efficiency of the developed method for accomplishing complex manipulation tasks. The results also showed that it could be generalized to fabrics with different colors and boxes with different sizes, textures, or geometric shapes.