Publication
ForwardDLO: Model-Based Bimanual Shape Matching of Unconstrained Deformable Linear Objects
Tim Missal; Berk Guler; Lucas Domingues; Simon Manschitz; Jan Peters; Paula Dornhofer Paro Costa
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2609.18455, Pages 1-8, arXiv, 2026.
Abstract
Ropes, cables, and other deformable linear ob-
jects appear in tasks from untangling to cable routing and
suturing, yet controlling their shape remains a challenge in
robot manipulation. We study model-based shape control in a
general setting: the object lies unfixated on a support surface
and two arms may grasp and move it anywhere along its
length. Because each arm chooses a grasp point, direction,
and magnitude, the joint action space is combinatorially large,
and the dynamics model’s per-prediction cost bounds how
much of it a planner can search. We present ForwardDLO,
a recurrent latent dynamics model for this unfixated bimanual
setting that predicts per-segment displacements grounded in the
observed rope state at every step. Our model reaches accuracy
comparable to more expensive baselines while containing no
explicit segment-to-segment operations, which makes batched
evaluation of candidate actions cheap. On open-loop prediction
of real rope motion it reaches the lowest error of the learned
models we evaluate, 13% below the strongest baseline. Within a
fixed time budget it scores 8 to 22 times more candidate actions
than models of comparable accuracy while matching them in
real-world shape matching; and on a simulated routing task
at a 30 Hz control rate, this throughput converts into 98%
task success versus at most 30% for the baselines at their
own budgets. We release the model, code, and a dataset of
2.42 million simulated and 14,107 real rope transitions in the
project’s repository.