CSGrasp: Category-Level Semantically-Aware Grasping Method

Abstract

Category-level grasping based on related tasks is an important research interest in robotic grasping, driven by substantial industrial demand. A robust evaluation mechanism and an accurate inference module are crucial components of the research. This article proposes a novel grasping algorithmic framework. This approach facilitates the acquisition of task-specific datasets and training without additional manual annotations. The algorithm establishes point-wise correspondence between grasp instances and the category-level prior model, achieving precise transfer of semantic information and grasp priors. Compared with previous similar work, this article truly realizes point-wise level knowledge transfer between grasp instances and priors, obtaining superior performance in category-level grasping tasks.