# Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks

arXiv: https://arxiv.org/abs/2203.15004 · HTML: https://mlsanity.com/paper/2203.15004/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2022.

## Built on by (most cited first, 17 of 17)

- [LLM-as-a-Verifier: A General-Purpose Verification Framework](https://mlsanity.com/paper/2607.05391/) (2026)
- [Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos](https://mlsanity.com/paper/2506.15680/) (2025)
- [Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration](https://mlsanity.com/paper/2309.09408/) (2023)
- [Few-Shot In-Context Imitation Learning via Implicit Graph Alignment](https://mlsanity.com/paper/2310.12238/) (2023)
- [GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2303.05193/) (2023)
- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [General-purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2408.08160/) (2024)
- [Learning Physics-Guided Residual Dynamics for Deformable Object Simulation](https://mlsanity.com/paper/2607.13451/) (2026)
- [Learning visual-based deformable object rearrangement with local graph neural networks](https://mlsanity.com/paper/2310.10307/) (2023)
- [DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time](https://mlsanity.com/paper/2502.15037/) (2025)
- [Certifiably Safe Manipulation of Deformable Linear Objects via Joint Shape and Tension Prediction](https://mlsanity.com/paper/2505.13889/) (2025)
- [Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions](https://mlsanity.com/paper/2605.02699/) (2026)
- [PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics](https://mlsanity.com/paper/2607.20653/) (2026)
- [Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation](https://mlsanity.com/paper/2309.07609/) (2023)
- [Deformable Linear Object Surface Placement Using Elastica Planning and Local Shape Control](https://mlsanity.com/paper/2503.08545/) (2025)
- [CaRoBio: 3D Cable Routing with a Bio-inspired Gripper Fingernail](https://mlsanity.com/paper/2508.09558/) (2025)
- [Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation](https://mlsanity.com/paper/2604.22102/) (2026)

## Abstract (arXiv)

Manipulating deformable linear objects by robots has a wide range of applications, e.g., manufacturing and medical surgery. To complete such tasks, an accurate dynamics model for predicting the deformation is critical for robust control. In this work, we deal with this challenge by proposing a hybrid offline-online method to learn the dynamics of cables in a robust and data-efficient manner. In the offline phase, we adopt Graph Neural Network (GNN) to learn the deformation dynamics purely from the simulation data. Then a linear residual model is learned in real-time to bridge the sim-to-real gap. The learned model is then utilized as the dynamics constraint of a trust region based Model Predictive Controller (MPC) to calculate the optimal robot movements. The online learning and MPC run in a closed-loop manner to robustly accomplish the task. Finally, comparative results with existing methods are provided to quantitatively show the effectiveness and robustness.