For the task of hanging clothes, learning how to insert a hanger into a garment is crucial but has been seldom explored in robotics. In this work, we address the problem of inserting a hanger into various unseen garments that are initially laid out flat on a table. This task is challenging due to its long-horizon nature, the high degrees of freedom of the garments, and the lack of data. To simplify the learning process, we first propose breaking the task into several stages. Then, we formulate each stage as a policy learning problem and propose low-dimensional action parameterization. To overcome the challenge of limited data, we build our own simulator and create 144 synthetic clothing assets to effectively collect high-quality training data. Our approach uses single-view depth images and object masks as input, which mitigates the Sim2Real appearance gap and achieves high generalization capabilities for new garments. Extensive experiments in both simulation and the real world validate our proposed method. By training on various garments in the simulator, our method achieves a 75\% success rate with 8 different unseen garments in the real world.

翻译：在衣物悬挂任务中，学习如何将衣架插入衣物至关重要，但该问题在机器人领域尚未得到充分探索。本研究致力于解决将衣架插入各类未见衣物的任务，这些衣物最初平铺于桌面上。该任务因其长时序特性、衣物的高自由度以及数据稀缺性而极具挑战性。为简化学习过程，我们首先提出将任务分解为若干阶段，进而将每个阶段建模为策略学习问题，并提出低维度动作参数化方法。为克服数据有限的挑战，我们构建了专用仿真器并创建了144个合成衣物资产，以高效收集高质量训练数据。我们的方法采用单视角深度图像与物体掩码作为输入，有效缓解了仿真到真实场景的外观差异，并对新衣物展现出优异的泛化能力。在仿真与真实场景中的大量实验验证了所提方法的有效性。通过在仿真器中训练多种衣物，我们的方法在真实场景中对8种未见衣物实现了75%的成功率。