Robotic Grasping from Classical to Modern: A Survey

TL;DR

This survey reviews the evolution of robotic grasping from classical methods to modern data-driven and semantic approaches, highlighting progress, challenges, and future directions for achieving human-level robotic manipulation.

Contribution

It provides a comprehensive overview of the history, current state, and future prospects of robotic grasping, integrating classical, data-driven, and semantic methods in a unified survey.

Findings

Classical analytic methods laid foundational principles for robotic grasping.

Recent data-driven approaches have significantly improved grasp success rates.

Semantic grasping advances enable more intelligent and autonomous robotic manipulation.

Abstract

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.