论文标题
从合成数据中学习折叠的合成数据的关键点
Learning Keypoints from Synthetic Data for Robotic Cloth Folding
论文作者
论文摘要
机器人布的操纵由于其可变形性而具有挑战性，这使得确定其全州不可行。但是，对于折叠，知道几个语义关键的位置就足够了。卷积神经网络（CNN）可用于检测这些关键点，但需要大量的带注释数据，这很昂贵。为了克服这一点，我们建议仅从合成数据中学习这些关键点检测器，从而实现低成本数据收集。在本文中，我们可以在过程中生成毛巾的图像，并使用它们来训练CNN。我们评估了该检测器在单人机器人设置上折叠毛巾的性能，并发现掌握和折叠成功率分别为77％和53％。我们得出的结论是，从合成数据中学习关键点探测器的布料折叠和相关任务是一个有希望的研究方向，讨论了一些失败并将其与未来的工作联系起来。该系统的视频以及代码库，有关CNN体系结构和培训设置的更多详细信息，请访问https://github.com/tlpss/workshop-icra-2022-cloth-keypoints.git。
Robotic cloth manipulation is challenging due to its deformability, which makes determining its full state infeasible. However, for cloth folding, it suffices to know the position of a few semantic keypoints. Convolutional neural networks (CNN) can be used to detect these keypoints, but require large amounts of annotated data, which is expensive to collect. To overcome this, we propose to learn these keypoint detectors purely from synthetic data, enabling low-cost data collection. In this paper, we procedurally generate images of towels and use them to train a CNN. We evaluate the performance of this detector for folding towels on a unimanual robot setup and find that the grasp and fold success rates are 77% and 53%, respectively. We conclude that learning keypoint detectors from synthetic data for cloth folding and related tasks is a promising research direction, discuss some failures and relate them to future work. A video of the system, as well as the codebase, more details on the CNN architecture and the training setup can be found at https://github.com/tlpss/workshop-icra-2022-cloth-keypoints.git.
