Feedback-based Fabric Strip Folding
This addresses the challenge of manipulating deformable objects like fabric for robotics applications, but it is incremental as it builds on existing folding methods.
The paper tackled the problem of robotic fabric strip folding by applying feedback-based control using a low-dimensional state from camera images, and it outperformed two state-of-the-art methods in accuracy.
Accurate manipulation of a deformable body such as a piece of fabric is difficult because of its many degrees of freedom and unobservable properties affecting its dynamics. To alleviate these challenges, we propose the application of feedback-based control to robotic fabric strip folding. The feedback is computed from the low dimensional state extracted from a camera image. We trained the controller using reinforcement learning in simulation which was calibrated to cover the real fabric strip behaviors. The proposed feedback-based folding was experimentally compared to two state-of-the-art folding methods and our method outperformed both of them in terms of accuracy.