# Deformable Linear Object Surface Placement Using Elastica Planning and Local Shape Control

arXiv: https://arxiv.org/abs/2503.08545 · HTML: https://mlsanity.com/paper/2503.08545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Challenges and Outlook in Robotic Manipulation of Deformable Objects](https://mlsanity.com/paper/2105.01767/) (2021)
- [Soft Robots Modeling: a Structured Overview](https://mlsanity.com/paper/2112.03645/) (2021)
- [Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks](https://mlsanity.com/paper/2203.15004/) (2022)
- [Autonomously Untangling Long Cables](https://mlsanity.com/paper/2207.07813/) (2022)
- [A Survey on Robotic Manipulation of Deformable Objects: Recent Advances, Open Challenges and New Frontiers](https://mlsanity.com/paper/2312.10419/) (2023)
- [A Coarse-to-Fine Framework for Dual-Arm Manipulation of Deformable Linear Objects with Whole-Body Obstacle Avoidance](https://mlsanity.com/paper/2209.11145/) (2022)
- [Dual Arm Steering of Deformable Linear Objects in 2-D and 3-D Environments Using Euler's Elastica Solutions](https://mlsanity.com/paper/2502.07509/) (2025)

## Abstract (arXiv)

Manipulation of deformable linear objects (DLOs) in constrained environments is a challenging task. This paper describes a two-layered approach for placing DLOs on a flat surface using a single robot hand. The high-level layer is a novel DLO surface placement method based on Euler's elastica solutions. During this process one DLO endpoint is manipulated by the robot gripper while a variable interior point of the DLO serves as the start point of the portion aligned with the placement surface. The low-level layer forms a pipeline controller. The controller estimates the DLO current shape using a Residual Neural Network (ResNet) and uses low-level feedback to ensure task execution in the presence of modeling and placement errors. The resulting DLO placement approach can recover from states where the high-level manipulation planner has failed as required by practical robot manipulation systems. The DLO placement approach is demonstrated with simulations and experiments that use silicon mock-up objects prepared for fresh food applications.