  Robotic Grasping has always been an active topic in robotics since grasping
is one of the fundamental but most challenging skills of robots. It demands the
coordination of robotic perception, planning, and control for robustness and
intelligence. However, current solutions are still far behind humans,
especially when confronting unstructured scenarios. In this paper, we survey
the advances of robotic grasping, starting from the classical formulations and
solutions to the modern ones. By reviewing the history of robotic grasping, we
want to provide a complete view of this community, and perhaps inspire the
combination and fusion of different ideas, which we think would be helpful to
touch and explore the essence of robotic grasping problems. In detail, we
firstly give an overview of the analytic methods for robotic grasping. After
that, we provide a discussion on the recent state-of-the-art data-driven
grasping approaches rising in recent years. With the development of computer
vision, semantic grasping is being widely investigated and can be the basis of
intelligent manipulation and skill learning for autonomous robotic systems in
the future. Therefore, in our survey, we also briefly review the recent
progress in this topic. Finally, we discuss the open problems and the future
research directions that may be important for the human-level robustness,
autonomy, and intelligence of robots.
