mlsanityData to 7 Oct 2026

Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects

arXiv 2403.10290 ↗First posted 15 Mar 2024cs.RO cs.SY eess.SYcited by 1

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

Deformable objects present several challenges to the field of robotic manipulation. One of the tasks that best encapsulates the difficulties arising due to non-rigid behavior is shape control, which requires driving an object to a desired shape. While shape-servoing methods have been shown successful in contexts with approximately linear behavior, they can fail in tasks with more complex dynamics. We investigate an alternative approach, using offline RL to solve a planar shape control problem of a Deformable Linear Object (DLO). To evaluate the effect of material properties, two DLOs are tested namely a soft rope and an elastic cord. We frame this task as a goal-conditioned offline RL problem, and aim to learn to generalize to unseen goal shapes. Data collection and augmentation procedures are proposed to limit the amount of experimental data which needs to be collected with the real robot. We evaluate the amount of augmentation needed to achieve the best results, and test the effect of regularization through behavior cloning on the TD3+BC algorithm. Finally, we show that the proposed approach is able to outperform a shape-servoing baseline in a curvature inversion experiment.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 22 of its 41 references that are in the map. First posted on arXiv 15 Mar 2024.

Builds on

22 of 41 references.

References · 22

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 882
  4. 2017Deep Reinforcement Learning that Matterscited by 727
  5. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 256
  6. 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 145
  7. 2018Sim-to-Real Reinforcement Learning for Deformable Object Manipulationcited by 124
  8. 2021d3rlpy: An Offline Deep Reinforcement Learning Librarycited by 84
  9. 2019Learning to Manipulate Deformable Objects without Demonstrationscited by 76
  10. 2021A Workflow for Offline Model-Free Robotic Reinforcement Learningcited by 68
  11. 2020Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Datacited by 51
  12. 2019Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objectscited by 44
10 more
  1. 2021Challenges and Outlook in Robotic Manipulation of Deformable Objectscited by 43
  2. 2020D2RL: Deep Dense Architectures in Reinforcement Learningcited by 40
  3. 2023What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?cited by 36
  4. 2019Dynamic Cloth Manipulation with Deep Reinforcement Learningcited by 26
  5. 2022Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approachcited by 5
  6. 2022Lattice-based shape tracking and servoing of elastic objectscited by 5
  7. 2023GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learningcited by 5
  8. 2023DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objectscited by 4
  9. 2023Multi Actor-Critic DDPG for Robot Action Space Decomposition: A Framework to Control Large 3D Deformation of Soft Linear Objectscited by 3
  10. 2022Learning Shape Control of Elastoplastic Deformable Linear Objectscited by 2

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025DiSA-IQL: Offline Reinforcement Learning for Robust Soft Robot Control under Distribution Shiftscited by 0

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Close in wording, no citation either way

  1. 2026Online Material Estimation for Conditioned Diffusion Policy in Shaping Deformable Linear Objects
  2. 2018Learning-based Feedback Controller for Deformable Object Manipulation
  3. 2025MoDeSuite: Robot Learning Task Suite for Benchmarking Mobile Manipulation with Deformable Objects
  4. 2025DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation
  5. 2022Learning Deformable Object Manipulation from Expert Demonstrations
  6. 2025Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy
  7. 2026Learning to unfold cloth: Scaling up world models to deformable object manipulation
  8. 2024SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy