# SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception

arXiv: https://arxiv.org/abs/2209.13706 · HTML: https://mlsanity.com/paper/2209.13706/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 27 of 27)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation](https://mlsanity.com/paper/1806.08756/) (2018)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Interactive Perception: Leveraging Action in Perception and Perception in Action](https://mlsanity.com/paper/1604.03670/) (2016)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [Learning to Manipulate Deformable Objects without Demonstrations](https://mlsanity.com/paper/1910.13439/) (2019)
- [Learning Predictive Representations for Deformable Objects Using Contrastive Estimation](https://mlsanity.com/paper/2003.05436/) (2020)
- [Learning Visible Connectivity Dynamics for Cloth Smoothing](https://mlsanity.com/paper/2105.10389/) (2021)
- [SpeedFolding: Learning Efficient Bimanual Folding of Garments](https://mlsanity.com/paper/2208.10552/) (2022)
- [Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data](https://mlsanity.com/paper/2003.01835/) (2020)
- [Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor](https://mlsanity.com/paper/1910.04854/) (2019)
- [Learning Robotic Manipulation through Visual Planning and Acting](https://mlsanity.com/paper/1905.04411/) (2019)
- [VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation](https://mlsanity.com/paper/2003.09044/) (2020)
- [Planar Robot Casting with Real2Sim2Real Self-Supervised Learning](https://mlsanity.com/paper/2111.04814/) (2021)
- [Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter](https://mlsanity.com/paper/1903.01588/) (2019)
- [Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience](https://mlsanity.com/paper/2010.03209/) (2020)
- [Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables](https://mlsanity.com/paper/2011.04840/) (2020)
- [Untangling Dense Knots by Learning Task-Relevant Keypoints](https://mlsanity.com/paper/2011.04999/) (2020)
- [Object Finding in Cluttered Scenes Using Interactive Perception](https://mlsanity.com/paper/1911.07482/) (2019)
- [Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies](https://mlsanity.com/paper/2107.08942/) (2021)
- [Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics](https://mlsanity.com/paper/2003.12698/) (2020)
- [Autonomously Untangling Long Cables](https://mlsanity.com/paper/2207.07813/) (2022)
- [Efficiently Learning Single-Arm Fling Motions to Smooth Garments](https://mlsanity.com/paper/2206.08921/) (2022)
- [Disentangling Dense Multi-Cable Knots](https://mlsanity.com/paper/2106.02252/) (2021)

## Built on by (most cited first, 5 of 5)

- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy](https://mlsanity.com/paper/2309.09051/) (2023)
- [Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulations](https://mlsanity.com/paper/2505.00500/) (2025)
- [Self-Supervised Learning for Interactive Perception of Surgical Thread for Autonomous Suture Tail-Shortening](https://mlsanity.com/paper/2307.06845/) (2023)
- [TWISTED-RL: Hierarchical Skilled Agents for Knot-Tying without Human Demonstrations](https://mlsanity.com/paper/2602.14526/) (2026)

## Abstract (arXiv)

Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling. This paper extends prior work on autonomously untangling long cables by introducing novel uncertainty quantification metrics and actions that interact with the cable to reduce perception uncertainty. We present Sliding and Grasping for Tangle Manipulation 2.0 (SGTM 2.0), a system that autonomously untangles cables approximately 3 meters in length with a bilateral robot using estimates of uncertainty at each step to inform actions. By interactively reducing uncertainty, Sliding and Grasping for Tangle Manipulation 2.0 (SGTM 2.0) reduces the number of state-resetting moves it must take, significantly speeding up run-time. Experiments suggest that SGTM 2.0 can achieve 83% untangling success on cables with 1 or 2 overhand and figure-8 knots, and 70% termination detection success across these configurations, outperforming SGTM 1.0 by 43% in untangling accuracy and 200% in full rollout speed. Supplementary material, visualizations, and videos can be found at sites.google.com/view/sgtm2.