解开密集的多电缆结
Disentangling Dense Multi-Cable Knots
摘要：
解开两个或多个电缆需要许多步骤来消除电缆之间和电缆内的交叉。我们正式解开多个电缆的问题，并提出了一种算法，迭代减少非平面多cAble kNots（IRON-MAN），输出机器人的行动，以消除交叉多电缆打结结构。我们实例化这个算法与一个学习的感知系统，灵感来自以前的工作在单电缆解开给定的图像输入，可以解开两个电缆扭曲，三个电缆编织，和两个或三个电缆的结，如上手，广场，卡里克弯曲，床单弯曲，冠，和渔夫的结。IRON-MAN跟踪与目标电缆端点和交叉点相对应的任务相关关键点，并通过识别和取消对结结构至关重要的交叉点来反复解开电缆。使用达芬奇手术机器人，我们通过实验评估了IRON-MAN在解开训练数据中出现的多缆线结类型以及推广到新类别的多缆线结上的有效性。结果表明，IRON-MAN可有效解开涉及多达三根电缆的结，成功率为80.5%，并可推广到培训期间不存在的结类型，电缆颜色不同或相同。
英文摘要：
Disentangling two or more cables requires many steps to remove crossings between and within cables. We formalize the problem of disentangling multiple cables and present an algorithm, Iterative Reduction Of Non-planar Multiple cAble kNots (IRON-MAN), that outputs robot actions to remove crossings from multi-cable knotted structures. We instantiate this algorithm with a learned perception system, inspired by prior work in single-cable untying that given an image input, can disentangle two-cable twists, three-cable braids, and knots of two or three cables, such as overhand, square, carrick bend, sheet bend, crown, and fisherman's knots. IRON-MAN keeps track of task-relevant keypoints corresponding to target cable endpoints and crossings and iteratively disentangles the cables by identifying and undoing crossings that are critical to knot structure. Using a da Vinci surgical robot, we experimentally evaluate the effectiveness of IRON-MAN on untangling multi-cable knots of types that appear in the training data, as well as generalizing to novel classes of multi-cable knots. Results suggest that IRON-MAN is effective in disentangling knots involving up to three cables with 80.5% success and generalizing to knot types that are not present during training, with cables of both distinct or identical colors.
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