  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.
