[论文解读] Robotic Grasping from Classical to Modern: A Survey

Hanbo Zhang, Jian Tang+2 more|arXiv (Cornell University)|Feb 8, 2022

Robot Manipulation and Learning被引用 22

一句话总结

本论文综述从经典解析方法到现代数据驱动和面向对象的方法在机器人抓取方面的进展，强调表示、方法、数据集以及尚待解决的挑战。

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.

研究动机与目标

• 全面展示机器人抓取从经典到现代方法的历史全貌。
• 讨论解析抓取综合、数据驱动方法和面向对象的抓取。
• 强调感知、规划与控制在鲁棒抓取中的作用。
• 识别尚待解决的问题与未来趋势，以实现接近人类水平的鲁棒性和语义抓取。

提出的方法

• 回顾带有力闭合和形闭合概念的解析抓取综合。
• 讨论包括模仿学习、基于采样的判别以及端到端学习在内的数据驱动抓取方法。
• 介绍面向对象的抓取和语义抓取的视角。
• 总结抓取表示形式（基于接触、SE(3)、2D/3D、像素级抓取地图）。
• 突出抓取研究中的数据集和评估概念。

实验结果

研究问题

• RQ1 机器人中抓取的形式化定义和表示是什么？
• RQ2 解析和数据驱动方法如何发展以解决抓取综合问题？
• RQ3 每种方法的权衡、优点和局限性是什么？
• RQ4 实现鲁棒、自治且具有语义基础的抓取有哪些有希望的未来方向？

主要发现

• 解析抓取综合依赖于形闭合和力闭合，具有多种接触模型，是奠定基础的。
• 数据驱动抓取主导了最近的进展，利用模仿学习、基于采样的判别和端到端学习。
• 面向对象和语义抓取正成为处理非结构化场景和人机语言接口的新方向。
• 自监督学习、仿真和域自适应被用于解决数据需求和仿真到现实的差距。
• 抓取表示形式从基于接触和 SE(3) 到像素级抓取地图，反映感知能力的演进。
• Dex-Net 与 Jacquard 等数据集在数据驱动抓取研究中发挥核心作用。