Precise shape control of Deformable Linear Objects (DLOs) is crucial in robotic applications such as industrial and medical fields. However, existing methods face challenges in handling complex large deformation tasks, especially those involving opposite curvatures, and lack efficiency and precision. To address this, we propose a two-stage framework combining Reinforcement Learning (RL) and online visual servoing. In the large-deformation stage, a model-based reinforcement learning approach using an ensemble of dynamics models is introduced to significantly improve sample efficiency. Additionally, we design a self-curriculum goal generation mechanism that dynamically selects intermediate-difficulty goals with high diversity through imagined evaluations, thereby optimizing the policy learning process. In the small-deformation stage, a Jacobian-based visual servo controller is deployed to ensure high-precision convergence. Simulation results show that the proposed method enables efficient policy learning and significantly outperforms mainstream baselines in shape control success rate and precision. Furthermore, the framework effectively transfers the policy trained in simulation to real-world tasks with zero-shot adaptation. It successfully completes all 30 cases with diverse initial and target shapes across DLOs of different sizes and materials. The project website is available at: https://anonymous.4open.science/w/sc-mbrl-dlo-EB48/

翻译：可变形线性物体（DLO）的精确形状控制在工业和医疗等机器人应用领域至关重要。然而，现有方法在处理复杂的大变形任务（尤其是涉及相反曲率的任务）时面临挑战，且缺乏效率与精度。为此，我们提出了一种结合强化学习（RL）与在线视觉伺服的两阶段框架。在大变形阶段，引入了一种基于集成动力学模型的模型强化学习方法，显著提升了样本效率。此外，我们设计了一种自课程目标生成机制，通过虚拟评估动态选择具有高多样性的中等难度中间目标，从而优化策略学习过程。在小变形阶段，部署了基于雅可比矩阵的视觉伺服控制器以确保高精度收敛。仿真结果表明，所提方法能够实现高效的策略学习，并在形状控制成功率和精度方面显著优于主流基线方法。此外，该框架能够将仿真环境中训练的策略以零样本适应方式有效迁移至实际任务中，成功完成了针对不同尺寸和材料DLO的30个具有多样初始形状和目标形状的测试案例。项目网站地址为：https://anonymous.4open.science/w/sc-mbrl-dlo-EB48/