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.
翻译:机器人精选法一直是机器人的一个积极话题,因为掌握是机器人最根本但最具挑战性的技能之一。它要求协调机器人的认知、规划和控制,以增进机能和智能。然而,目前的解决办法仍然远远落后于人类,特别是在应对无结构的情景时。在本文件中,我们从古典的配方和现代的解决方案开始,调查机器人捕捉的进步。通过审查机器人捕捉的历史,我们想提供对这一社区的完整观点,或许可以激励不同想法的结合和融合,我们认为,接触和探索机器人捕捉问题的本质是有益的。我们首先简要回顾机器人捕捉的解析方法。之后,我们讨论近些年来以最新状态数据驱动的掌握方法不断上升。随着计算机视觉的发展,语义捕捉正在受到广泛调查,并且可以成为未来自主机器人系统智能操作和技能学习的基础。因此,在我们的调查中,我们还简要地审视了机器人捕捉问题的最新进展。最后,我们讨论了人类智能的开放程度,我们讨论了这一研究的开放程度。