# Feedback-based Fabric Strip Folding

## TL;DR

This paper introduces a feedback-based control method for robotic fabric strip folding, utilizing reinforcement learning and visual feedback to improve accuracy over existing methods.

## Contribution

It presents a novel feedback control approach trained in simulation that effectively handles fabric deformability and outperforms current state-of-the-art techniques.

## Key findings

- Outperforms existing fabric folding methods in accuracy
- Uses reinforcement learning trained in simulation
- Employs visual feedback from camera images

## Abstract

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.