Due to the deformability of garments, generating a large amount of high-quality data for robotic garment manipulation tasks is highly challenging. In this paper, we present a synthetic garment dataset that can be used for robotic garment folding. We begin by constructing geometric garment templates based on keypoints and applying generative models to generate realistic texture patterns. Leveraging these keypoint annotations, we generate folding demonstrations in simulation and train folding policies via closed-loop imitation learning. To improve robustness, we propose KG-DAgger, which uses a keypoint-based strategy to generate demonstration data for recovering from failures. KG-DAgger significantly improves the model performance, boosting the real-world success rate by 25\%. After training with 15K trajectories (about 2M image-action pairs), the model achieves a 75\% success rate in the real world. Experiments in both simulation and real-world settings validate the effectiveness of our proposed framework.

翻译：由于衣物的可变形特性，为机器人衣物操作任务生成大量高质量数据极具挑战性。本文提出一个可用于机器人衣物折叠的合成衣物数据集。我们首先基于关键点构建几何衣物模板，并应用生成模型生成逼真的纹理图案。利用这些关键点标注，我们在仿真中生成折叠演示数据，并通过闭环模仿学习训练折叠策略。为提高鲁棒性，我们提出KG-DAgger方法，该方法采用基于关键点的策略来生成用于从失败中恢复的演示数据。KG-DAgger显著提升了模型性能，将现实世界成功率提高了25%。经过15K条轨迹（约200万图像-动作对）的训练后，模型在现实世界中达到了75%的成功率。仿真与真实环境中的实验均验证了所提框架的有效性。