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，过去数十年间已被广泛研究。传统方法通过分析物体的物理模型并计算力封闭抓取，但需要预先获取物体的完整三维模型，而这往往难以实现。近年来，随着机器学习的显著进展，数据驱动方法在该领域占据主导地位。尽管此类方法取得了令人瞩目的改进，但其依赖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过程，直接从物体点云中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，以表征物体表面的局部几何特征。随后，利用超二次曲面的三轴对称性，从每个恢复的曲面合成一系列对向抓取。设计评估模型对候选抓取质量进行量化评价，并选取评分最高的候选作为最终抓取姿态。我们在孤立场景与堆叠场景中开展实验，验证了方法的有效性。结果表明，无需完整模型或预先训练，本方法的表现可与现有最前沿技术相媲美。