Dynamic Cloth Folding Using Curriculum Learning

This paper presents a novel algorithm for training robotic arms to manipulate cloth, by leveraging reinforcement learning and curriculum learning approaches. Traditional cloth manipulation algorithms rely heavily on predefined action primitives and assumptions about cloth dynamics, introducing significant prior knowledge. To circumvent this limitation, we utilize reinforcement learning to train our cloth folding agent. To fully utilize the advantage of reinforcement learning, we propose a semi-sparse reward function incorporating folding accuracy and a curriculum scheme to accelerate training and improve policy stability. We validate the proposed method by implementing it in the StableBaselines3 framework and training the agent using the soft actor critic algorithm in our virtual environment based on physical-based cloth simulator. Our results demonstrate the benefits of the curriculum learning scheme which increases sample efficiency and accelerates training process compared with previous reinforcement learning cloth manipulation method.