An overview of learning-based dexterous grasping: recent advances and future directions

Abstract

Recently, the practical implications of dexterous grasping technology have become a key point of research in robotics and artificial intelligence. At its core, this technology aims to empower robots to achieve human-level grasping capabilities. To help researchers quickly acquire the latest advancements, we have conducted a comprehensive review of the recent research developments, focusing on learning-based approaches, from two perspectives: Grasp Generation (GG) and Grasp Execution (GE). Specifically, GG refers to generating appropriate grasping poses for the target object. GE refers to executing grasp poses by motion planning and motion control. Afterwards, we introduce recent benchmark datasets and evaluation metrics. Based on these extensive benchmarks, we offer a comparative analysis of the state-of-the-art solutions. Lastly, we highlight several research directions that need to be further addressed, which will greatly facilitate the practical deployment of dexterous grasping technology in industrial manufacturing, household services, medical rehabilitation, etc. We believe it is a crucial area of research for future progress in robotic manipulation.