Reinforcement Learning-Based Routing Framework Guided by Vision-Language Model

Multistage cable routing requires a robot to successfully navigate a cable through a series of clips, which is a challenging task. Due to the unpredictability of cable deformation and the complexity of aligning cables with clip openings, traditional model-based or imitation learning methods are difficult to achieve satisfactory results. To address the difficulty, we propose a novel reinforcement learning-based routing framework guided by a vision-language model (VLM). The core of the proposed framework is to establish the description of the spatial position constraints between cables and clips in VLMs to produce a high-level plan that incorporates grasping and routing points, which then guides the local routing strategy. The local routing strategy is trained by human-in-the-loop reinforcement learning, enabling the model to learn a strategy that reduces deformation when interacting with clips. Our method is validated on a Franka robot across multistage cable routing tasks, and it outperforms some baselines in terms of reliability and generalization.