This repo contains a survey on robotic grasping:
Robotic Grasping from Classical to Modern: A Survey
As a Ph.D. student working on the topic of robotic grasping, I found it really an interesting but challenging area. It is with essentially pracitical background and a lot of excellent researchers have been devoted to it. By this survey, I want to summarize the interesting ideas, papers, and projects. Hopefully it can inspire the future by reviewing the past.
This survey will be updated continuously, and unstable versions are also available in this repo. Besides, I will also update the included references and their links if I have time on this page. Certainly, when it achieves a milestone, I will also update the new version on ArXiv. If you have any other suggestions, please do not hesitate to contact me: zhanghanbo163 [at] stu [dot] xjtu [dot] edu [dot] cn
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.
- Update the paper list.
- Abstract the figures for representative algorithms.
- Add performance comparison of open-sourced methods.
@article{zhang2022robotic,
title={Robotic Grasping from Classical to Modern: A Survey},
author={Zhang, Hanbo and Tang, Jian and Sun, Shiguang and Lan, Xuguang},
journal={arXiv preprint arXiv:2202.03631},
year={2022}
}