无需训练即可实现可解释的一只手打结，对遮挡和初始状态变化鲁棒。
KnotDLO: Toward Interpretable Knot Tying
- 基于当前绳索形状规划抓取与目标点，分段连续计算抓取姿态。
- 在16次未见配置的尝试中，成功打成单结率达50%。
- 不依赖人类示范或训练，适合研究可解释机器人操作的场景。
本文提出KnotDLO，一种针对单手柔性线状物体（DLO）打结的方法，具有抗遮挡、适应不同初始绳索配置、生成可解释运动策略的优点，且无需人类示范或训练。系统根据当前绳索形状规划抓取位置和未来状态的目标点，抓取姿态通过追踪分段线性曲线并结合当前形状索引计算，保证分段连续性。中间路径点则由当前状态与目标状态的几何关系推导得出。该系统将视觉推理与控制解耦。在16次未见过的绳索初始配置下进行打结实验，成功完成单结的比例达到50%。
原文摘要 · Abstract (English)
This work presents KnotDLO, a method for one-handed Deformable Linear Object (DLO) knot tying that is robust to occlusion, repeatable for varying rope initial configurations, interpretable for generating motion policies, and requires no human demonstrations or training. Grasp and target waypoints for future DLO states are planned from the current DLO shape. Grasp poses are computed from indexing the tracked piecewise linear curve representing the DLO state based on the current curve shape and are piecewise continuous. KnotDLO computes intermediate waypoints from the geometry of the current DLO state and the desired next state. The system decouples visual reasoning from control. In 16 trials of knot tying, KnotDLO achieves a 50% success rate in tying an overhand knot from previously unseen configurations.
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