We show that it is possible to learn an open-loop policy in simulation for the dynamic manipulation of a deformable linear object (DLO) -- e.g., a rope, wire, or cable -- that can be executed by a real robot without additional training. Our method is enabled by integrating an existing state-of-the-art DLO model (Discrete Elastic Rods) with MuJoCo, a robot simulator. We describe how this integration was done, check that validation results produced in simulation match what we expect from analysis of the physics, and apply policy optimization to train an open-loop policy from data collected only in simulation that uses a robot arm to fling a wire precisely between two obstacles. This policy achieves a success rate of 76.7% when executed by a real robot in hardware experiments without additional training on the real task.

翻译：我们证明，在仿真环境中学习可变形线性物体（DLO，如绳索、金属丝或线缆）的动态开环策略是可行的，该策略可直接由真实机器人执行而无需额外训练。我们的方法通过将现有最先进的DLO模型（离散弹性杆模型）与机器人仿真器MuJoCo进行集成来实现。本文描述了集成过程，验证了仿真输出结果与物理分析预期的一致性，并应用策略优化方法，仅通过仿真数据训练出一个开环策略——使机器人臂精确地将金属丝甩过两个障碍物之间。该策略在硬件实验中由真实机器人执行时，无需针对真实任务进行额外训练即可达到76.7%的成功率。