Robotic grasping is an essential and fundamental task and has been studied extensively over the past several decades. Traditional work analyzes physical models of the objects and computes force-closure grasps. Such methods require pre-knowledge of the complete 3D model of an object, which can be hard to obtain. Recently with significant progress in machine learning, data-driven methods have dominated the area. Although impressive improvements have been achieved, those methods require a vast amount of training data and suffer from limited generalizability. In this paper, we propose a novel two-stage approach to predicting and synthesizing grasping poses directly from the point cloud of an object without database knowledge or learning. Firstly, multiple superquadrics are recovered at different positions within the object, representing the local geometric features of the object surface. Subsequently, our algorithm exploits the tri-symmetry feature of superquadrics and synthesizes a list of antipodal grasps from each recovered superquadric. An evaluation model is designed to assess and quantify the quality of each grasp candidate. The grasp candidate with the highest score is then selected as the final grasping pose. We conduct experiments on isolated and packed scenes to corroborate the effectiveness of our method. The results indicate that our method demonstrates competitive performance compared with the state-of-the-art without the need for either a full model or prior training.

翻译：机器人抓取是一项基础且关键的任务，在过去数十年中得到了广泛研究。传统方法通过分析物体物理模型计算力封闭抓取，这需要预先获取物体的完整三维模型，而此类模型往往难以获得。近年来，随着机器学习的显著进展，数据驱动方法成为该领域主流。尽管这些方法取得了令人瞩目的改进，但需要大量训练数据且泛化能力有限。本文提出一种新颖的两阶段方法，无需数据库知识或学习，即可直接从物体点云预测并合成抓取姿态。首先，在物体内部不同位置恢复多个超二次曲面，用以表征物体表面的局部几何特征。随后，算法利用超二次曲面的三轴对称性，从每个恢复的曲面中合成一组对极抓取候选。我们设计了评估模型来量化每个候选抓取的质量，并选取评分最高的作为最终抓取姿态。在孤立和堆叠场景上的实验验证了该方法的效果。结果表明，我们的方法无需完整模型或预训练，即可展现出与最先进技术相媲美的性能。