Tracing - estimating the spatial state of - long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factories, construction, transportation, and healthcare. For long deformable linear objects (DLOs or simply cables) with many (over 25) crossings, we present HANDLOOM (Heterogeneous Autoregressive Learned Deformable Linear Object Observation and Manipulation), a learning-based algorithm that fits a trace to a greyscale image of cables. We evaluate HANDLOOM on semi-planar DLO configurations where each crossing involves at most 2 segments. HANDLOOM makes use of neural networks trained with 30,000 simulated examples and 568 real examples to autoregressively estimate traces of cables and classify crossings. Experiments find that in settings with multiple identical cables, HANDLOOM can trace each cable with 80% accuracy. In single-cable images, HANDLOOM can trace and identify knots with 77% accuracy. When HANDLOOM is incorporated into a bimanual robot system, it enables state-based imitation of knot tying with 80% accuracy, and it successfully untangles 64% of cable configurations across 3 levels of difficulty. Additionally, HANDLOOM demonstrates generalization to knot types and materials (rubber, cloth rope) not present in the training dataset with 85% accuracy. Supplementary material, including all code and an annotated dataset of RGB-D images of cables along with ground-truth traces, is at https://sites.google.com/view/cable-tracing.

翻译：追踪（即估计长柔性线性物体如线缆、线束、软管或绳索的空间状态）对于家庭、零售、工厂、建筑、运输和医疗等领域的广泛任务具有实用价值。针对具有大量（超过25个）交叉点的长柔性线性物体（以下简称DLOs或线缆），我们提出了HANDLOOM（异质自回归学习型柔性线线性物体观测与操控算法），这是一种基于学习的算法，能够将线缆灰度图像拟合为追踪轨迹。我们在每个交叉点最多涉及两段线缆的半平面DLO构型上评估了HANDLOOM。该算法利用经30,000个模拟样本和568个真实样本训练的神经网络，自回归地估计线缆轨迹并对交叉点进行分类。实验发现，在存在多条相同线缆的场景中，HANDLOOM对每条线缆的追踪准确率达80%。在单线缆图像中，HANDLOOM对线缆结的追踪与识别准确率达77%。当将HANDLOOM集成至双臂机器人系统时，其支持基于状态的打结模仿，准确率达80%，并能在三个难度级别上成功解开64%的线缆构型。此外，HANDLOOM对训练数据集中未出现的结类型和材料（橡胶、布绳）展现出85%的泛化准确率。补充材料（包括全部代码及带真实轨迹标注的线缆RGB-D图像数据集）参见 https://sites.google.com/view/cable-tracing。