AssistDLO: Assistive Teleoperation for Deformable Linear Object Manipulation

2026-05-07

AI Summary

This work addresses the challenges of state perception and manipulation of deformable linear objects (DLOs) in teleoperation, where depth uncertainty complicates control. To overcome this, the authors propose an assistive teleoperation framework integrating multi-view real-time state estimation, visual augmentation, and geometry-aware shared control. The core innovation is the design of SA-CBF, the first geometry-aware shared autonomy controller, which acts as a skill equalizer to significantly enhance novice performance. In a dual-arm rope-untying experiment involving 22 participants, SA-CBF increased task success rates among novices from 71% to 88%, with particularly pronounced benefits for rigid ropes. In contrast, experts preferred visual assistance, and long, soft ropes showed greater improvement with visual support, highlighting the critical influence of user expertise and material properties on the efficacy of assistance strategies.

Abstract

Manipulating Deformable Linear Objects (DLOs) is challenging in robotics due to their infinite-dimensional configuration space and complex nonlinear dynamics. In teleoperation, depth uncertainty hinders state perception and reaction. AssistDLO addresses this challenge as an assistive teleoperation 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.

Problem

• Deformable Linear Objects
• teleoperation
• state perception
• shared autonomy
• manipulation

Innovation

• shared autonomy
• Control Barrier Functions
• deformable linear objects
• teleoperation
• geometry-aware control