Abstract
In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly reduces the prediction dimensions and accelerates the learning process. We present an effective end-to-end network, CMG-Net, for grasping unknown objects in a cluttered environment by efficiently predicting multi-finger grasp poses and hand configurations from a single-shot point cloud. Moreover, we create a synthetic grasp dataset that consists of five thousand cluttered scenes, 80 object categories, and 20 million annotations. We perform a comprehensive empirical study and demonstrate the effectiveness of our grasping representation and CMG-Net. Our work significantly outperforms the state-of-the-art for three-finger robotic hands. We also demonstrate that the model trained using synthetic data performs very well for real robots.
Abstract (translated)
在本文中，我们提出了一种利用多指机器人手与待操纵物体之间的接触关系进行 grasping 的新表示方法。这种表示方法极大地减少了预测维度，并加速了学习过程。我们介绍了一种有效的端到端网络 CMG-Net，用于在复杂环境中识别未知物体，该网络从一次点云图像中高效预测多指抓握姿态和手构型。此外，我们创造了一个合成的抓握数据集，其中包括五千个复杂场景、80个物体类别和2000万注释。我们进行了全面的实证研究，并证明了我们的 grasping 表示和 CMG-Net 的有效性。我们的工作 significantly outperforms 三维机器人手的技能水平。我们还证明了使用合成数据训练的模型对于真实机器人非常良好地表现。
URL
https://arxiv.org/abs/2303.13182
