用视觉与动态规划结合，让机械臂在杂乱中抓移动物体
Adaptive Grasping of Moving Objects in Dense Clutter via Global-to-Local Detection and Static-to-Dynamic Planning
- 从全局检测转为局部检测，适应复杂环境
- 动态规划优化提升抓取效率，实时响应物体运动
- 无需大量训练，可通用多种物体和运动速度
机器人抓取面临非静态物体状态、未知物性及密集排列等多重现实不确定性，现有学习方法在不同条件下表现不一。本文提出一种基于相似性匹配的方法，仅使用单个RGBD相机，在物体同时运动且密集堆积的场景下实现对新物体的自适应抓取。通过将视觉检测从全局转向局部，抓取规划从静态转为动态，并引入优化方法提升时间敏感任务的规划效率。实验表明，该系统无需大量训练即可适应多种物体类型、布局和运动速度。视频演示见：https://youtu.be/sdC50dx-xp8?si=27oVr4dhG0rqN_tT。
原文摘要 · Abstract (English)
Robotic grasping is facing a variety of real-world uncertainties caused by non-static object states, unknown object properties, and cluttered object arrangements. The difficulty of grasping increases with the presence of more uncertainties, where commonly used learning-based approaches struggle to perform consistently across varying conditions. In this study, we integrate the idea of similarity matching to tackle the challenge of grasping novel objects that are simultaneously in motion and densely cluttered using a single RGBD camera, where multiple uncertainties coexist. We achieve this by shifting visual detection from global to local states and operating grasp planning from static to dynamic scenes. Notably, we introduce optimization methods to enhance planning efficiency for this time-sensitive task. Our proposed system can adapt to various object types, arrangements and movement speeds without the need for extensive training, as demonstrated by real-world experiments. Videos are available at https://youtu.be/sdC50dx-xp8?si=27oVr4dhG0rqN_tT.
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