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.

翻译：本文提出一种利用多指机械手与被操作物体之间的接触进行抓取的新型表示方法。该表示显著降低了预测维度并加速了学习过程。我们提出了一种高效的端到端网络CMG-Net，通过从单次点云数据中高效预测多指抓取姿态和手部构型，实现对杂乱环境中的未知物体进行抓取。此外，我们构建了一个包含五千个杂乱场景、80个物体类别和2000万个标注样本的合成抓取数据集。通过全面的实证研究，我们验证了所提出的抓取表示与CMG-Net的有效性。我们的方法在三指机械手任务上显著优于现有最先进技术。我们还证明，使用合成数据训练的模型在真实机器人上表现优异。