# Self-Curriculum Model-based Reinforcement Learning for Shape Control of Deformable Linear Objects

arXiv: https://arxiv.org/abs/2602.21816 · HTML: https://mlsanity.com/paper/2602.21816/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 13 of 13)

- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [Automatic Goal Generation for Reinforcement Learning Agents](https://mlsanity.com/paper/1705.06366/) (2017)
- [Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids](https://mlsanity.com/paper/1810.01566/) (2018)
- [Goal-Conditioned Reinforcement Learning: Problems and Solutions](https://mlsanity.com/paper/2201.08299/) (2022)
- [RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks](https://mlsanity.com/paper/2205.02909/) (2022)
- [Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects](https://mlsanity.com/paper/1911.06283/) (2019)
- [Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills](https://mlsanity.com/paper/2310.19424/) (2023)
- [Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approach](https://mlsanity.com/paper/2205.04004/) (2022)
- [Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning](https://mlsanity.com/paper/2206.06719/) (2022)
- [Multi Actor-Critic DDPG for Robot Action Space Decomposition: A Framework to Control Large 3D Deformation of Soft Linear Objects](https://mlsanity.com/paper/2312.04308/) (2023)
- [Learning Shape Control of Elastoplastic Deformable Linear Objects](https://mlsanity.com/paper/2208.02067/) (2022)
- [Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects](https://mlsanity.com/paper/2403.10290/) (2024)
- [Robotic Control of the Deformation of Soft Linear Objects Using Deep Reinforcement Learning](https://mlsanity.com/paper/2312.05056/) (2023)

## Abstract (arXiv)

Precise shape control of Deformable Linear Objects (DLOs) is crucial in robotic applications such as industrial and medical fields. However, existing methods face challenges in handling complex large deformation tasks, especially those involving opposite curvatures, and lack efficiency and precision. To address this, we propose a two-stage framework combining Reinforcement Learning (RL) and online visual servoing. In the large-deformation stage, a model-based reinforcement learning approach using an ensemble of dynamics models is introduced to significantly improve sample efficiency. Additionally, we design a self-curriculum goal generation mechanism that dynamically selects intermediate-difficulty goals with high diversity through imagined evaluations, thereby optimizing the policy learning process. In the small-deformation stage, a Jacobian-based visual servo controller is deployed to ensure high-precision convergence. Simulation results show that the proposed method enables efficient policy learning and significantly outperforms mainstream baselines in shape control success rate and precision. Furthermore, the framework effectively transfers the policy trained in simulation to real-world tasks with zero-shot adaptation. It successfully completes all 30 cases with diverse initial and target shapes across DLOs of different sizes and materials. The project website is available at: https://anonymous.4open.science/w/sc-mbrl-dlo-EB48/