# Cloth Manipulation Using Random-Forest-Based Imitation Learning

arXiv: https://arxiv.org/abs/1802.09661 · HTML: https://mlsanity.com/paper/1802.09661/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 27 Feb 2018.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 8 of 8)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration](https://mlsanity.com/paper/1603.06348/) (2016)
- [Folding Deformable Objects using Predictive Simulation and Trajectory Optimization](https://mlsanity.com/paper/1512.06922/) (2015)
- [Learning Visual Servoing with Deep Features and Fitted Q-Iteration](https://mlsanity.com/paper/1703.11000/) (2017)
- [3D Deformable Object Manipulation using Fast Online Gaussian Process Regression](https://mlsanity.com/paper/1709.07218/) (2017)
- [Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary](https://mlsanity.com/paper/1710.06947/) (2017)
- [Multi-Sensor Surface Analysis for Robotic Ironing](https://mlsanity.com/paper/1602.04918/) (2016)

## Built on by (most cited first, 7 of 7)

- [VisuoSpatial Foresight for Physical Sequential Fabric Manipulation](https://mlsanity.com/paper/2102.09754/) (2021)
- [QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation](https://mlsanity.com/paper/2303.13320/) (2023)
- [Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap](https://mlsanity.com/paper/2103.02554/) (2021)
- [CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2507.19983/) (2025)
- [MoDeSuite: Robot Learning Task Suite for Benchmarking Mobile Manipulation with Deformable Objects](https://mlsanity.com/paper/2507.21796/) (2025)
- [Co-manipulation of soft-materials estimating deformation from depth images](https://mlsanity.com/paper/2301.05609/) (2023)
- [Robotic Fabric Flattening with Wrinkle Direction Detection](https://mlsanity.com/paper/2303.04909/) (2023)

## Abstract (arXiv)

We present a novel approach for robust manipulation of high-DOF deformable objects such as cloth. Our approach uses a random forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random forest-based controller is determined automatically based on the training data consisting visual features and optimal control actions. This enables us to integrate the overall process of training data classification and controller optimization into an imitation learning (IL) approach. Our approach enables learning of robust control policy for cloth manipulation with guarantees on convergence.We have evaluated our approach on different multi-task cloth manipulation benchmarks such as flattening, folding and twisting. In practice, our approach works well with different deformable features learned based on the specific task or deep learning. Moreover, our controller outperforms a simple or piecewise linear controller in terms of robustness to noise. In addition, our approach is easy to implement and does not require much parameter tuning.