Learning Visual Feedback Control for Dynamic Cloth Folding

Abstract

Robotic manipulation of cloth is a challenging task due to the high dimensionality of the configuration space and the complexity of dynamics affected by various material properties. The effect of complex dynamics is even more pronounced in dynamic folding, for example, when a square piece of fabric is folded in two by a single manipulator. To account for the complexity and uncertainties, feedback of the cloth state using e.g. vision is typically needed. However, construction of visual feedback policies for dynamic cloth folding is an open problem.

In this paper, we present a solution that learns policies in simulation using Reinforcement Learning (RL) and transfers the learned policies directly to the real world. In addition, to learn a single policy that manipulates multiple materials, we randomize the material properties in simulation. We evaluate the contributions of visual feedback and material randomization in real-world experiments. The experimental results demonstrate that the proposed solution can fold successfully different fabric types using dynamic manipulation in the real world. 

Video

Below is a video overview of the problem and the solution we propose in the paper.

Method Overview

The cloth folding policies are trained in simulation and transferred directly to the real world. The transfer is enabled by using Domain Randomization (DR) and  an identical Cartesian controller both in Sim and Real.

Experiments

We train 3 different policies

• Fixed

  • Trained in simulation without visual feedback (full states) and  without cloth dynamics randomization

  • Tested in the real world without visual feedback (fixed trajectories from simulation success)

• Visual

  • Trained in simulation with visual feedback, domain randomization, and no cloth dynamics randomization

  • Tested in the real world with visual feedback

• Proposed Method

  • Trained in simulation with visual feedback, domain randomization, and cloth dynamics randomization

  • Tested in the real world with visual feedback

Fixed Policy

X Visual Feedback 

X Cloth dynamics randomization

Visual Policy

✓ Visual Feedback 

X Cloth dynamics randomization

Proposed Method

✓ Visual Feedback 

✓ Cloth dynamics randomization

Results

The results indicate that using both visual feedback and dynamics domain randomization is essential for consistent performance across different materials.

Mean trajectories from the different policies also show that only the policy trained with dynamics domain randomization is able to react to the differences in behavior caused by the different dynamics.

Citation

To cite this work, please use the following BibTex entry:

@inproceedings{hietala2022closing,

  author={Hietala, Julius and Blanco–Mulero, David and Alcan, Gokhan and Kyrki, Ville},

  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 

  title={Learning Visual Feedback Control for Dynamic Cloth Folding}, 

  year={2022},

  volume={},

  number={},

  pages={1455-1462},

  doi={10.1109/IROS47612.2022.9981376}}