# KnotDLO: Toward Interpretable Knot Tying

arXiv: https://arxiv.org/abs/2506.22176 · HTML: https://mlsanity.com/paper/2506.22176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Zero-Shot Visual Imitation](https://mlsanity.com/paper/1804.08606/) (2018)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data](https://mlsanity.com/paper/2003.01835/) (2020)
- [Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects](https://mlsanity.com/paper/1911.06283/) (2019)
- [Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints](https://mlsanity.com/paper/2011.00627/) (2020)
- [Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units](https://mlsanity.com/paper/2106.00898/) (2021)
- [Disentangling Dense Multi-Cable Knots](https://mlsanity.com/paper/2106.02252/) (2021)
- [Learning Topological Motion Primitives for Knot Planning](https://mlsanity.com/paper/2009.02615/) (2020)
- [Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approach](https://mlsanity.com/paper/2205.04004/) (2022)
- [Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Models](https://mlsanity.com/paper/2109.11091/) (2021)
- [MultiDLO: Simultaneous Shape Tracking of Multiple Deformable Linear Objects with Global-Local Topology Preservation](https://mlsanity.com/paper/2310.13245/) (2023)
- [The Robustness of Tether Friction in Non-idealized Terrains](https://mlsanity.com/paper/2208.10646/) (2022)

## Abstract (arXiv)

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.