用关键点生成虚拟衣物数据，让机器人学会自适应折叠。
FoldNet: Learning Generalizable Closed-Loop Policy for Garment Folding via Keypoint-Driven Asset and Demonstration Synthesis
- 基于关键点构建虚拟衣物模板并合成纹理与折叠示范。
- 训练15K轨迹后真实世界成功率75%，失败恢复提升25%。
- 适合做柔性物体抓取与闭环控制的科研人员参考。
由于衣物的可变形性，为机器人衣物操作任务生成大量高质量数据极为困难。本文提出一种可用于机器人折叠的合成衣物数据集。首先基于关键点构建几何衣物模板，并利用生成模型合成逼真的纹理图案。借助这些关键点标注，在仿真中生成折叠示范，并通过闭环模仿学习训练折叠策略。为提升鲁棒性，提出KG-DAgger方法，采用基于关键点的策略生成失败恢复示范数据。该方法显著提升模型性能，使真实世界成功率提高25%。在15,000条轨迹（约200万图像-动作对）上训练后，模型在真实场景中达到75%的成功率。仿真与真实环境实验均验证了所提框架的有效性。
原文摘要 · Abstract (English)
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.
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