[2510.19268] Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models

Computer Science > Robotics arXiv:2510.19268 (cs) [Submitted on 22 Oct 2025 (v1), last revised 15 Apr 2026 (this version, v2)] Title:Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models Authors:Mingen Li, Houjian Yu, Yixuan Huang, Youngjin Hong, Hantao Ye, Changhyun Choi View a PDF of the paper titled Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models, by Mingen Li and 5 other authors View PDF HTML (experimental) Abstract: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 rec...