  Vision-based robotic cloth unfolding has made great progress recently.
However, prior works predominantly rely on value learning and have not fully
explored policy-based techniques. Recently, the success of reinforcement
learning on the large language model has shown that the policy gradient
algorithm can enhance policy with huge action space. In this paper, we
introduce ClothPPO, a framework that employs a policy gradient algorithm based
on actor-critic architecture to enhance a pre-trained model with huge 10^6
action spaces aligned with observation in the task of unfolding clothes. To
this end, we redefine the cloth manipulation problem as a partially observable
Markov decision process. A supervised pre-training stage is employed to train a
baseline model of our policy. In the second stage, the Proximal Policy
Optimization (PPO) is utilized to guide the supervised model within the
observation-aligned action space. By optimizing and updating the strategy, our
proposed method increases the garment's surface area for cloth unfolding under
the soft-body manipulation task. Experimental results show that our proposed
framework can further improve the unfolding performance of other
state-of-the-art methods.
