This paper extends prior work on untangling long cables and presents TUSK (Tracing to Untangle Semi-planar Knots), a learned cable-tracing algorithm that resolves over-crossings and undercrossings to recognize the structure of knots and grasp points for untangling from a single RGB image. This work focuses on semi-planar knots, which are knots composed of crossings that each include at most 2 cable segments. We conduct experiments on long cables (3 m in length) with up to 15 semi-planar crossings across 6 different knot types. Crops of crossings from 3 knots (overhand, figure 8, and bowline) of the 6 are seen during training, but none of the full knots are seen during training. This is an improvement from prior work on long cables that can only untangle 2 knot types. Experiments find that in settings with multiple identical cables, TUSK can trace a single cable with 81% accuracy on 7 new knot types. In single-cable images, TUSK can trace and identify the correct knot with 77% success on 3 new knot types. We incorporate TUSK into a bimanual robot system and find that it successfully untangles 64% of cable configurations, including those with new knots unseen during training, across 3 levels of difficulty. Supplementary material, including an annotated dataset of 500 RGB-D images of a knotted cable along with ground-truth traces, can be found at https://sites.google.com/view/tusk-rss.

翻译：本文在先前长电缆解缠工作的基础上进行拓展，提出TUSK（追踪以解缠半平面绳结）——一种基于学习的电缆追踪算法。该算法通过识别交叉点的上下重叠关系，从单张RGB图像中解析绳结结构并确定解缠抓取点。本研究聚焦半平面绳结，这类绳结由多个交叉点组成，每个交叉点最多涉及两段电缆。我们在长度达3米、包含6种不同类型共15个半平面交叉点的长电缆上开展实验。训练过程中仅见过从3种绳结（单结、八字结和称人结）中截取的交叉局部图像，未接触任何完整绳结形态。相比先前仅能解缠2种绳结类型的长电缆研究，这是重要突破。实验表明：在多根相同电缆共存场景下，TUSK能准确追踪目标单根电缆，对7种新型绳结的识别准确率达81%；在单电缆图像场景下，对3种新型绳结的追踪与正确识别成功率为77%。我们将TUSK集成至双臂机器人系统，在三种难度等级下，该系统成功解缠64%的电缆构型，其中包含训练中未见的新型绳结。补充材料（含500张带地面真值追踪标注的绳结电缆RGB-D图像数据集）可访问 https://sites.google.com/view/tusk-rss 获取。