# DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time

arXiv: https://arxiv.org/abs/2502.15037 · HTML: https://mlsanity.com/paper/2502.15037/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [TossingBot: Learning to Throw Arbitrary Objects with Residual Physics](https://mlsanity.com/paper/1903.11239/) (2019)
- [AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation](https://mlsanity.com/paper/2407.07889/) (2024)
- [Cable Manipulation with a Tactile-Reactive Gripper](https://mlsanity.com/paper/1910.02860/) (2019)
- [Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects](https://mlsanity.com/paper/1911.06283/) (2019)
- [Challenges and Outlook in Robotic Manipulation of Deformable Objects](https://mlsanity.com/paper/2105.01767/) (2021)
- [Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of Deformable Objects](https://mlsanity.com/paper/2203.00663/) (2022)
- [Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks](https://mlsanity.com/paper/2203.15004/) (2022)
- [Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects](https://mlsanity.com/paper/2406.05931/) (2024)
- [Simultaneous Learning of Contact and Continuous Dynamics](https://mlsanity.com/paper/2310.12054/) (2023)
- [Can't Touch This: Real-Time, Safe Motion Planning and Control for Manipulators Under Uncertainty](https://mlsanity.com/paper/2301.13308/) (2023)
- [Let's Make a Splan: Risk-Aware Trajectory Optimization in a Normalized Gaussian Splat](https://mlsanity.com/paper/2409.16915/) (2024)
- [Differentiable Robotic Manipulation of Deformable Rope-like Objects Using Compliant Position-based Dynamics](https://mlsanity.com/paper/2202.09714/) (2022)
- [Sim2Real Neural Controllers for Physics-based Robotic Deployment of Deformable Linear Objects](https://mlsanity.com/paper/2303.02574/) (2023)
- [Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities](https://mlsanity.com/paper/2309.13745/) (2023)

## Built on by (most cited first, 1 of 1)

- [TrackDeform3D: Markerless and Autonomous 3D Keypoint Tracking and Dataset Collection for Deformable Objects](https://mlsanity.com/paper/2603.17068/) (2026)

## Abstract (arXiv)

Autonomous wire harness assembly requires robots to manipulate complex branched cables with high precision and reliability. A key challenge in automating this process is predicting how these flexible and branched structures behave under manipulation. Without accurate predictions, it is difficult for robots to reliably plan or execute assembly operations. While existing research has made progress in modeling single-threaded Deformable Linear Objects (DLOs), extending these approaches to Branched Deformable Linear Objects (BDLOs) presents fundamental challenges. The junction points in BDLOs create complex force interactions and strain propagation patterns that cannot be adequately captured by simply connecting multiple single-DLO models. To address these challenges, this paper presents Differentiable discrete branched Elastic rods for modeling Furcated DLOs in real-Time (DEFT), a novel framework that combines a differentiable physics-based model with a learning framework to: 1) accurately model BDLO dynamics, including dynamic propagation at junction points and grasping in the middle of a BDLO, 2) achieve efficient computation for real-time inference, and 3) enable planning to demonstrate dexterous BDLO manipulation. A comprehensive series of real-world experiments demonstrates DEFT's efficacy in terms of accuracy, computational speed, and generalizability compared to state-of-the-art alternatives. Project page:https://roahmlab.github.io/DEFT/.