# Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research

arXiv: https://arxiv.org/abs/2204.10297 · HTML: https://mlsanity.com/paper/2204.10297/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [RoboNet: Large-Scale Multi-Robot Learning](https://mlsanity.com/paper/1910.11215/) (2019)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/2011.07215/) (2020)
- [Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention](https://mlsanity.com/paper/2104.11203/) (2021)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021)
- [Learning to Manipulate Deformable Objects without Demonstrations](https://mlsanity.com/paper/1910.13439/) (2019)
- [ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots](https://mlsanity.com/paper/1909.11639/) (2019)
- [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://mlsanity.com/paper/2003.01314/) (2020)
- [FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy](https://mlsanity.com/paper/2111.05623/) (2021)
- [LazyDAgger: Reducing Context Switching in Interactive Imitation Learning](https://mlsanity.com/paper/2104.00053/) (2021)
- [Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor](https://mlsanity.com/paper/1910.04854/) (2019)
- [VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation](https://mlsanity.com/paper/2003.09044/) (2020)
- [Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger](https://mlsanity.com/paper/2108.09779/) (2021)
- [TriFinger: An Open-Source Robot for Learning Dexterity](https://mlsanity.com/paper/2008.03596/) (2020)
- [The Robotarium: A remotely accessible swarm robotics research testbed](https://mlsanity.com/paper/1609.04730/) (2016)
- [REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning](https://mlsanity.com/paper/1905.07447/) (2019)
- [Quantifying the Reality Gap in Robotic Manipulation Tasks](https://mlsanity.com/paper/1811.01484/) (2018)
- [Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation](https://mlsanity.com/paper/2105.02087/) (2021)
- [Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics](https://mlsanity.com/paper/2003.12698/) (2020)
- [Solving the Real Robot Challenge using Deep Reinforcement Learning](https://mlsanity.com/paper/2109.15233/) (2021)
- [Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge](https://mlsanity.com/paper/2101.02842/) (2021)
- [Dexterous Manipulation Primitives for the Real Robot Challenge](https://mlsanity.com/paper/2101.11597/) (2021)

## Built on by (most cited first, 6 of 6)

- [Visuotactile Affordances for Cloth Manipulation with Local Control](https://mlsanity.com/paper/2212.05108/) (2022)
- [Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention](https://mlsanity.com/paper/2301.03003/) (2023)
- [Learning Fabric Manipulation in the Real World with Human Videos](https://mlsanity.com/paper/2211.02832/) (2022)
- [IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors](https://mlsanity.com/paper/2306.15228/) (2023)
- [Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance](https://mlsanity.com/paper/2509.03889/) (2025)
- [DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning](https://mlsanity.com/paper/2410.21758/) (2024)

## Abstract (arXiv)

Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware. Using Reach, a cloud robotics platform that enables low-latency remote execution of control policies on physical robots, we present the first systematic benchmarking of fabric manipulation algorithms on physical hardware. We develop 4 novel learning-based algorithms that model expert actions, keypoints, reward functions, and dynamic motions, and we compare these against 4 learning-free and inverse dynamics algorithms on the task of folding a crumpled T-shirt with a single robot arm. The entire lifecycle of data collection, model training, and policy evaluation is performed remotely without physical access to the robot workcell. Results suggest a new algorithm combining imitation learning with analytic methods achieves 84% of human-level performance on the folding task. See https://sites.google.com/berkeley.edu/cloudfolding for all data, code, models, and supplemental material.