论文标题
布区域分割以良好的掌握选择
Cloth Region Segmentation for Robust Grasp Selection
论文作者
论文摘要
布料检测和操纵是家庭和工业环境中的一项常见任务，但是由于布的可变形性，这些任务仍然是机器人的挑战。此外，在许多与布料相关的任务（如洗衣折叠和床制造）中，这对于操纵特定区域（如边缘和角落）至关重要，而不是折叠。在这项工作中，我们专注于细分和抓住这些关键区域的问题。我们的方法训练网络，以将布的边缘和拐角与深度图像分割，从而将这些区域与皱纹或褶皱区分开。我们还提供了一种新型算法，用于估计分割的抓地位置，方向和方向性不确定性。我们在真实的机器人系统上演示了我们的方法，并表明它优于基线方法在掌握成功方面的基线方法。视频和其他补充材料可在以下网址提供：https：//sites.google.com/view/cloth-segnementation。
Cloth detection and manipulation is a common task in domestic and industrial settings, yet such tasks remain a challenge for robots due to cloth deformability. Furthermore, in many cloth-related tasks like laundry folding and bed making, it is crucial to manipulate specific regions like edges and corners, as opposed to folds. In this work, we focus on the problem of segmenting and grasping these key regions. Our approach trains a network to segment the edges and corners of a cloth from a depth image, distinguishing such regions from wrinkles or folds. We also provide a novel algorithm for estimating the grasp location, direction, and directional uncertainty from the segmentation. We demonstrate our method on a real robot system and show that it outperforms baseline methods on grasping success. Video and other supplementary materials are available at: https://sites.google.com/view/cloth-segmentation.
