mlsanityData to 14 Oct 2026

Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

arXiv 1806.07851 ↗First posted 20 Jun 2018cs.RO cs.AI cs.LGcited by 131

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Abstract (arXiv)

We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. To-date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed further, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating deformable objects (specifically cloth). We evaluate our approach on three tasks --- folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects.

Key facts. Cited by 131 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Jun 2018.

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Citing papers per year: 2018: 1; 2019: 2; 2020: 1; 2021: 26; 2022: 26; 2023: 21; 2024: 22; 2025: 21; 2026: 11.

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  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 490
  2. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 179
  3. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 176
  4. 2021Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisationcited by 122
  5. 2021PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physicscited by 103
  6. 2024Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulationcited by 93
  7. 2021Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulationcited by 85
  8. 2020COG: Connecting New Skills to Past Experience with Offline Reinforcement Learningcited by 83
  9. 2021Understanding Domain Randomization for Sim-to-real Transfercited by 80
  10. 2022RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networkscited by 72
  11. 2021A Workflow for Offline Model-Free Robotic Reinforcement Learningcited by 69
  12. 2024SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splattingcited by 69
38 more
  1. 2018Task-Embedded Control Networks for Few-Shot Imitation Learningcited by 66
  2. 2021Robot Learning from Randomized Simulations: A Reviewcited by 63
  3. 2021Learning Visible Connectivity Dynamics for Cloth Smoothingcited by 62
  4. 2021Auto-Tuned Sim-to-Real Transfercited by 49
  5. 2019Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisorcited by 44
  6. 2022Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulationcited by 34
  7. 2022DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objectscited by 31
  8. 2024BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmarkcited by 30
  9. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 28
  10. 2026World-Gymnast: Training Robots with Reinforcement Learning in a World Modelcited by 27
  11. 2022Phone2Proc: Bringing Robust Robots Into Our Chaotic Worldcited by 26
  12. 2024Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgerycited by 26
  13. 2021Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systemscited by 24
  14. 2023Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AIcited by 23
  15. 2024UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondencecited by 23
  16. 2023Causal Policy Gradient for Whole-Body Mobile Manipulationcited by 21
  17. 2024Natural Language Can Help Bridge the Sim2Real Gapcited by 21
  18. 2021BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Imagecited by 20
  19. 2023Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policiescited by 20
  20. 2021Domain Adversarial Reinforcement Learningcited by 19
  21. 2021Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translationcited by 19
  22. 2022Learning Deformable Object Manipulation from Expert Demonstrationscited by 19
  23. 2025Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulationcited by 19
  24. 2019Scaling data-driven robotics with reward sketching and batch reinforcement learningcited by 18
  25. 2021VisuoSpatial Foresight for Physical Sequential Fabric Manipulationcited by 18
  26. 2021Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objectscited by 17
  27. 2022Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Realcited by 17
  28. 2023Benchmarking the Sim-to-Real Gap in Cloth Manipulationcited by 17
  29. 2024Continuous Control with Coarse-to-fine Reinforcement Learningcited by 17
  30. 2025SIMS-V: Simulated Instruction-Tuning for Spatial Video Understandingcited by 16
  31. 2023Grounding Graph Network Simulators using Physical Sensor Observationscited by 15
  32. 2022Contact Points Discovery for Soft-Body Manipulations with Differentiable Physicscited by 14
  33. 2022Coarse-to-Fine Q-attention with Learned Path Rankingcited by 13
  34. 2026VLANeXt: Recipes for Building Strong VLA Modelscited by 12
  35. 2021Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policiescited by 11
  36. 2021A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulationcited by 11
  37. 2024Learning Keypoints for Robotic Cloth Manipulation using Synthetic Datacited by 11
  38. 2024Green Screen Augmentation Enables Scene Generalisation in Robotic Manipulationcited by 11

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