  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.
