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Tracing - estimating the spatial state of - long deformable linear objectssuch as cables, threads, hoses, or ropes, is useful for a broad range of tasksin homes, retail, factories, construction, transportation, and healthcare. Forlong deformable linear objects (DLOs or simply cables) with many (over 25)crossings, we present HANDLOOM (Heterogeneous Autoregressive Learned DeformableLinear Object Observation and Manipulation), a learning-based algorithm thatfits a trace to a greyscale image of cables. We evaluate HANDLOOM onsemi-planar DLO configurations where each crossing involves at most 2 segments.HANDLOOM makes use of neural networks trained with 30,000 simulated examplesand 568 real examples to autoregressively estimate traces of cables andclassify crossings. Experiments find that in settings with multiple identicalcables, HANDLOOM can trace each cable with 80% accuracy. In single-cableimages, HANDLOOM can trace and identify knots with 77% accuracy. When HANDLOOMis incorporated into a bimanual robot system, it enables state-based imitationof knot tying with 80% accuracy, and it successfully untangles 64% of cableconfigurations across 3 levels of difficulty. Additionally, HANDLOOMdemonstrates 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 alongwith ground-truth traces, is at https://sites.google.com/view/cable-tracing.

HANDLOOM: Learned Tracing of One-Dimensional Objects for Inspection and Manipulation