Long-horizon routing tasks of deformable linear objects (DLOs), such as cables and ropes, are common in industrial assembly lines and everyday life. These tasks are particularly challenging because they require robots to manipulate DLO with long-horizon planning and reliable skill execution. Successfully completing such tasks demands adapting to their nonlinear dynamics, decomposing abstract routing goals, and generating multi-step plans composed of multiple skills, all of which require accurate high-level reasoning during execution. In this paper, we propose a fully autonomous hierarchical framework for solving challenging DLO routing tasks. Given an implicit or explicit routing goal expressed in language, our framework leverages vision-language models~(VLMs) for in-context high-level reasoning to synthesize feasible plans, which are then executed by low-level skills trained via reinforcement learning. To improve robustness over long horizons, we further introduce a failure recovery mechanism that reorients the DLO into insertion-feasible states. Our approach generalizes to diverse scenes involving object attributes, spatial descriptions, implicit language commands, and \myred{extended 5-clip settings}. It achieves an overall success rate of 92\% across long-horizon routing scenarios. Please refer to our project page: https://icra2026-dloroute.github.io/DLORoute/

翻译：长时域可变形线性物体（如电缆和绳索）的路由任务是工业装配线及日常生活中的常见操作。这类任务极具挑战性，要求机器人通过长时域规划与可靠技能执行来操控可变形线性物体。成功完成此类任务需适应其非线性动力学特性、分解抽象路由目标、并生成由多项技能组成的多步骤规划，所有这些都要求在执行过程中具备精确的高层推理能力。本文提出了一种全自主层级化框架，用于解决具有挑战性的可变形线性物体路由任务。针对以自然语言表达的隐式或显式路由目标，该框架利用视觉-语言模型进行上下文中的高层推理，以综合可行的任务规划，并由通过强化学习训练的低层技能执行。为提升长时域任务的鲁棒性，我们进一步引入故障恢复机制，将可变形线性物体重新导向至可插入状态。该方法可泛化至涉及物体属性、空间描述、隐式语言指令及扩展5夹场景的多样化环境。在长时域路由场景中，整体成功率达92%。项目页面详见：https://icra2026-dloroute.github.io/DLORoute/