mlsanityData to 7 Oct 2026

Disentangling Dense Multi-Cable Knots

arXiv 2106.02252 ↗First posted 4 Jun 2021cs.RO cs.AI cs.LGcited by 7

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Abstract (arXiv)

Disentangling two or more cables requires many steps to remove crossings between and within cables. We formalize the problem of disentangling multiple cables and present an algorithm, Iterative Reduction Of Non-planar Multiple cAble kNots (IRON-MAN), that outputs robot actions to remove crossings from multi-cable knotted structures. We instantiate this algorithm with a learned perception system, inspired by prior work in single-cable untying that given an image input, can disentangle two-cable twists, three-cable braids, and knots of two or three cables, such as overhand, square, carrick bend, sheet bend, crown, and fisherman's knots. IRON-MAN keeps track of task-relevant keypoints corresponding to target cable endpoints and crossings and iteratively disentangles the cables by identifying and undoing crossings that are critical to knot structure. Using a da Vinci surgical robot, we experimentally evaluate the effectiveness of IRON-MAN on untangling multi-cable knots of types that appear in the training data, as well as generalizing to novel classes of multi-cable knots. Results suggest that IRON-MAN is effective in disentangling knots involving up to three cables with 80.5% success and generalizing to knot types that are not present during training, with cables of both distinct or identical colors.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 4 Jun 2021.

Builds on

14 of 32 references.

References · 14

  1. 2018Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Controlcited by 220
  2. 2018Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulationcited by 140
  3. 2020SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulationcited by 118
  4. 2018Zero-Shot Visual Imitationcited by 114
  5. 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
  6. 2020Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networkscited by 77
  7. 2020Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Datacited by 51
  8. 2019Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objectscited by 44
  9. 2020VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulationcited by 40
  10. 2020Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experiencecited by 24
  11. 2020Untangling Dense Knots by Learning Task-Relevant Keypointscited by 21
  12. 2021Occlusion-robust Deformable Object Tracking without Physics Simulationcited by 15
2 more
  1. 2020Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabricscited by 10
  2. 2021Deformable Linear Object Prediction Using Locally Linear Latent Dynamicscited by 7

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 0; 2025: 2.

7 papers.

  1. 2023Multi-Stage Cable Routing through Hierarchical Imitation Learningcited by 59
  2. 2023KITE: Keypoint-Conditioned Policies for Semantic Manipulationcited by 39
  3. 2022Autonomously Untangling Long Cablescited by 9
  4. 2022SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perceptioncited by 5
  5. 2022All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicatorscited by 2
  6. 2025DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splattingcited by 0
  7. 2025KnotDLO: Toward Interpretable Knot Tyingcited by 0