# Dynamic Cloth Manipulation with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1910.14475 · HTML: https://mlsanity.com/paper/1910.14475/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 29 of 29)

- [FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy](https://mlsanity.com/paper/2111.05623/) (2021)
- [Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation](https://mlsanity.com/paper/2303.11057/) (2023)
- [UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence](https://mlsanity.com/paper/2405.06903/) (2024)
- [Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation](https://mlsanity.com/paper/2503.11999/) (2025)
- [VisuoSpatial Foresight for Physical Sequential Fabric Manipulation](https://mlsanity.com/paper/2102.09754/) (2021)
- [Benchmarking the Sim-to-Real Gap in Cloth Manipulation](https://mlsanity.com/paper/2310.09543/) (2023)
- [SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation](https://mlsanity.com/paper/2309.08728/) (2023)
- [TraKDis: A Transformer-based Knowledge Distillation Approach for Visual Reinforcement Learning with Application to Cloth Manipulation](https://mlsanity.com/paper/2401.13362/) (2024)
- [In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning](https://mlsanity.com/paper/2103.09402/) (2021)
- [Goal-Aware Generative Adversarial Imitation Learning from Imperfect Demonstration for Robotic Cloth Manipulation](https://mlsanity.com/paper/2209.10149/) (2022)
- [Learning Fabric Manipulation in the Real World with Human Videos](https://mlsanity.com/paper/2211.02832/) (2022)
- [GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2303.05193/) (2023)
- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [Grasping Deformable Objects via Reinforcement Learning with Cross-Modal Attention to Visuo-Tactile Inputs](https://mlsanity.com/paper/2504.15595/) (2025)
- [Semantic State Estimation in Cloth Manipulation Tasks](https://mlsanity.com/paper/2203.11647/) (2022)
- [Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning](https://mlsanity.com/paper/2502.11375/) (2025)
- [One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes](https://mlsanity.com/paper/2503.01092/) (2025)
- [Actively Learning Costly Reward Functions for Reinforcement Learning](https://mlsanity.com/paper/2211.13260/) (2022)
- [Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects](https://mlsanity.com/paper/2403.10290/) (2024)
- [Towards Real-World Efficiency: Domain Randomization in Reinforcement Learning for Pre-Capture of Free-Floating Moving Targets by Autonomous Robots](https://mlsanity.com/paper/2406.06460/) (2024)
- [Robotic Paper Wrapping by Learning Force Control](https://mlsanity.com/paper/2503.15685/) (2025)
- [Cloth Manipulation Planning on Basis of Mesh Representations with Incomplete Domain Knowledge and Voxel-to-Mesh Estimation](https://mlsanity.com/paper/2103.08137/) (2021)
- [Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation](https://mlsanity.com/paper/2305.03259/) (2023)
- [Learning to bag with a simulation-free reinforcement learning framework for robots](https://mlsanity.com/paper/2310.14398/) (2023)
- [The dGLI Cloth Coordinates: A Topological Representation for Semantic Classification of Cloth States](https://mlsanity.com/paper/2209.09191/) (2022)
- [Robotic Fabric Flattening with Wrinkle Direction Detection](https://mlsanity.com/paper/2303.04909/) (2023)
- [Topological reconstruction of sampled surfaces via Morse theory](https://mlsanity.com/paper/2405.17257/) (2024)
- [Object Manipulation in Marine Environments using Reinforcement Learning](https://mlsanity.com/paper/2406.03223/) (2024)
- [Disentangling perception and reasoning for improving data efficiency in learning cloth manipulation without demonstrations](https://mlsanity.com/paper/2601.21713/) (2026)

## Abstract (arXiv)

In this paper we present a Deep Reinforcement Learning approach to solve dynamic cloth manipulation tasks. Differing from the case of rigid objects, we stress that the followed trajectory (including speed and acceleration) has a decisive influence on the final state of cloth, which can greatly vary even if the positions reached by the grasped points are the same. We explore how goal positions for non-grasped points can be attained through learning adequate trajectories for the grasped points. Our approach uses few demonstrations to improve control policy learning, and a sparse reward approach to avoid engineering complex reward functions. Since perception of textiles is challenging, we also study different state representations to assess the minimum observation space required for learning to succeed. Finally, we compare different combinations of control policy encodings, demonstrations, and sparse reward learning techniques, and show that our proposed approach can learn dynamic cloth manipulation in an efficient way, i.e., using a reduced observation space, a few demonstrations, and a sparse reward.