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.

翻译：操控可变形线性物体（DLO）在机器人领域极具挑战性，因其具有无限维构型空间及复杂的非线性动力学特性。在遥操作中，深度不确定性会阻碍状态感知与反应。为此，AssistDLO提出了一种面向DLO操控的辅助遥操作框架，该框架融合了实时多视角状态估计、视觉辅助（VA）以及基于控制障碍函数的几何感知共享自主控制器（SA-CBF）。传统共享自主方法常依赖简单几何吸引子，难以保持DLO几何形态，而SA-CBF作为几何感知漏斗，既能实现精准抓取，又保留了操作员的高层级控制权。通过使用不同长度与刚度的绳索，在双手打结任务中开展了包含22名受试者的用户研究。结果表明：辅助效果显著依赖于操作员熟练度与DLO属性。SA-CBF对新手用户增益最强，可作为技能均衡器将任务成功率从71%提升至88%，且对较硬绳索更为有效；而专家用户更偏好VA，高柔顺长绳索更依赖视觉支持而非局部动作辅助。最终，这些发现表明有效的DLO遥操作不能依赖固定策略，亟需开发自适应、感知用户特性与材料特性的共享自主方法。