mlsanityData to 7 Oct 2026

Autonomously Untangling Long Cables

arXiv 2207.07813 ↗First posted 16 Jul 2022cs.RO cs.AIcited by 9

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Abstract (arXiv)

Cables are ubiquitous in many settings and it is often useful to untangle them. However, cables are prone to self-occlusions and knots, making them difficult to perceive and manipulate. The challenge increases with cable length: long cables require more complex slack management to facilitate observability and reachability. In this paper, we focus on autonomously untangling cables up to 3 meters in length using a bilateral robot. We develop RGBD perception and motion primitives to efficiently untangle long cables and novel gripper jaws specialized for this task. We present Sliding and Grasping for Tangle Manipulation (SGTM), an algorithm that composes these primitives to iteratively untangle cables with success rates of 67% on isolated overhand and figure-eight knots and 50% on more complex configurations. Supplementary material, visualizations, and videos can be found at https://sites.google.com/view/rss-2022-untangling/home.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 23 of its 31 references that are in the map. First posted on arXiv 16 Jul 2022.

Builds on

23 of 31 references.

References · 23

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  4. 2018Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulationcited by 140
  5. 2018Sim-to-Real Reinforcement Learning for Deformable Object Manipulationcited by 124
  6. 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
  7. 2021FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfoldingcited by 80
  8. 2020Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networkscited by 77
  9. 2019Learning to Manipulate Deformable Objects without Demonstrationscited by 76
  10. 2020Learning Predictive Representations for Deformable Objects Using Contrastive Estimationcited by 72
  11. 2021Learning Visible Connectivity Dynamics for Cloth Smoothingcited by 61
  12. 2021FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policycited by 54
11 more
  1. 2020Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Datacited by 51
  2. 2019Cable Manipulation with a Tactile-Reactive Grippercited by 44
  3. 2019Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisorcited by 44
  4. 2020VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulationcited by 40
  5. 2019Learning Robotic Manipulation through Visual Planning and Actingcited by 39
  6. 2020Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experiencecited by 24
  7. 2020Untangling Dense Knots by Learning Task-Relevant Keypointscited by 21
  8. 2021SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereocited by 18
  9. 2021Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policiescited by 11
  10. 2020Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabricscited by 10
  11. 2021Disentangling Dense Multi-Cable Knotscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 1; 2025: 1; 2026: 2.

9 papers.