Flattening Clothes with a Single-Arm Robot Based on Reinforcement Learning

Using reinforcement learning has enabled robots to learn how to accomplish a wide range of tasks without explicit instructions. In this paper, we use a single-arm robot for the flattening of a piece of cloth which is crumpled and placed on a table. We create a simulation environment with a gripper and a piece of cloth to learn a policy for the robot to choose the best action based on the observation of the environment. The policy is then transferred to a real robot and successfully tested. We also introduce our method on the recognition of the corners of the cloth using computer vision which includes comparing classic computer vision approach to a deep learning one. We use an ABB robot and a 2D camera for the experiments and PyBullet software for the simulation.