Publication
AssistDLO: Assistive Teleoperation for Deformable Linear Object Manipulation
Berk Guler; Simon Manschitz; Kay Pompetzki; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2605.06323, Pages 1-20, arXiv, 2026.
Abstract
Manipulating Deformable Linear Objects (DLOs) is
challenging in robotics due to their infinite-dimensional config-
uration space and complex nonlinear dynamics. In teleopera-
tion, depth uncertainty hinders state perception and reaction.
AssistDLO addresses this challenge as an assistive teleopera-
tion framework for DLO manipulation that combines real-
time multi-view state estimation, visual assistance (VA), and a
geometry-aware shared-autonomy controller based on Control
Barrier Functions (SA-CBF). While traditional shared autonomy
methods often rely on simple geometric attractors and may
fail to preserve DLO geometry, SA-CBF acts as a geometry-
aware funnel, facilitating precise grasping while preserving the
operator’s high-level authority. The framework is evaluated in a
bimanual knot-untangling user study (N = 22) using ropes with
varying length and rigidity. Results show that the effectiveness of
the assistance depends strongly on operator expertise and DLO
properties. SA-CBF provides the strongest gains for naive users,
acting as a skill equalizer that increases task success from 71%
to 88%, and is effective for stiffer ropes. Conversely, expert users
prefer VA, and highly compliant, long ropes benefit more from
visual support than localized action assistance. Ultimately, these
findings demonstrate that effective DLO teleoperation cannot rely
on a fixed strategy, highlighting the critical need for adaptive,
user-aware, and material-aware shared autonomy.