Towards Generalization and Data Efficient Learning of Deep Robotic Grasping

Chen, Zhuo; Lin, Mengxiang; Jia, Zhixin; Jian, Shibo

doi:10.48550/arxiv.2007.00982

Cited by 1 publication

(1 citation statement)

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“…It is also possible to directly bind grasps onto objects when synthesizing grasping. It can be achieved by combining grasp detection with object recognition [85,106,268] or semantic segmentation [3,4,9,35,56,57,136,172]. An alternative way is using reinforcement learning to encourage actions of grasping a specified object [103].…”

Section: Object-specific Grasp Synthesismentioning

confidence: 99%

Robotic Grasping from Classical to Modern: A Survey

Zhang¹,

Tang²,

Sun³

et al. 2022

Preprint

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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-ofthe-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.

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