# Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects

arXiv: https://arxiv.org/abs/2406.05931 · HTML: https://mlsanity.com/paper/2406.05931/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2024.

## Built on by (most cited first, 10 of 10)

- [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)
- [Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation](https://mlsanity.com/paper/2606.14188/) (2026)
- [DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time](https://mlsanity.com/paper/2502.15037/) (2025)
- [XPG-RL: Reinforcement Learning with Explainable Priority Guidance for Efficiency-Boosted Mechanical Search](https://mlsanity.com/paper/2504.20969/) (2025)
- [Certifiably Safe Manipulation of Deformable Linear Objects via Joint Shape and Tension Prediction](https://mlsanity.com/paper/2505.13889/) (2025)
- [Multi-Modal Zero-Shot Prediction of Color Trajectories in Food Drying](https://mlsanity.com/paper/2512.06190/) (2025)
- [TrackDeform3D: Markerless and Autonomous 3D Keypoint Tracking and Dataset Collection for Deformable Objects](https://mlsanity.com/paper/2603.17068/) (2026)
- [Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation](https://mlsanity.com/paper/2604.22102/) (2026)
- [Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs](https://mlsanity.com/paper/2610.14342/) (2026)

## Abstract (arXiv)

This paper addresses the task of modeling Deformable Linear Objects (DLOs), such as ropes and cables, during dynamic motion over long time horizons. This task presents significant challenges due to the complex dynamics of DLOs. To address these challenges, this paper proposes differentiable Discrete Elastic Rods For deformable linear Objects with Real-time Modeling (DEFORM), a novel framework that combines a differentiable physics-based model with a learning framework to model DLOs accurately and in real-time. The performance of DEFORM is evaluated in an experimental setup involving two industrial robots and a variety of sensors. A comprehensive series of experiments demonstrate the efficacy of DEFORM in terms of accuracy, computational speed, and generalizability when compared to state-of-the-art alternatives. To further demonstrate the utility of DEFORM, this paper integrates it into a perception pipeline and illustrates its superior performance when compared to the state-of-the-art methods while tracking a DLO even in the presence of occlusions. Finally, this paper illustrates the superior performance of DEFORM when compared to state-of-the-art methods when it is applied to perform autonomous planning and control of DLOs. Project page: https://roahmlab.github.io/DEFORM/.