仅需一次人类示范，就能让机械臂学会动态抓绳打结。
Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control
- 通过迭代优化任务空间误差，直接在真实硬件上学习
- 10次尝试内对7种不同绳索实现100%成功打结
- 可跨绳类快速迁移，2-5次即可适应新绳子
我们提出一种面向动态绳索操作的任务级迭代学习控制方法，以解决非平面绳结任务（飞结）。仅需一次人类示范和简化绳模型，即可在真实硬件上直接学习，无需大量演示数据或仿真。每次迭代中，算法通过求解二次规划反向推导机器人与绳的模型，将任务空间误差转化为动作更新。我们在7种不同类型绳索上进行评估，包括链条、乳胶手术管、编织绳和扭绳，绳径7–25毫米，密度0.013–0.5千克/米。学习过程在10次尝试内对所有绳索达到100%成功率。此外，该方法可在多数绳类间实现快速迁移，仅需2–5次尝试即可成功适配。
原文摘要 · Abstract (English)
We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of ropes. We demonstrate this method on a non-planar rope manipulation task called the flying knot. Using a single human demonstration and a simplified rope model, the method learns directly on hardware without reliance on large amounts of demonstration data or massive amounts of simulation. At each iteration, the algorithm inverts a model of the robot and rope by solving a quadratic program to propagate task-space errors into action updates. We evaluate performance across 7 different kinds of ropes, including chain, latex surgical tubing, and braided and twisted ropes, ranging in thicknesses of 7--25\,mm and densities of 0.013--0.5\,kg/m. Learning achieves a 100\% success rate within 10 trials on all ropes. Furthermore, the method can successfully transfer between most rope types in 2--5 trials. https://flying-knots.github.io
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