Learning Visual Feedback Control for Dynamic Cloth Folding

Julius Hietala, David Blanco Mulero*, Gökhan Alcan, Ville Kyrki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

13 Citations (Scopus)


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. Code, data, and videos are available at https://sites.google.com/view/dynamic-cloth-folding
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Number of pages8
ISBN (Electronic)978-1-6654-7927-1
Publication statusPublished - 26 Dec 2022
MoE publication typeA4 Conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - Kyoto, Japan
Duration: 23 Oct 202227 Nov 2022

Publication series

NameProceedings of the IEEE/RSJ international conference on intelligent robots and systems
ISSN (Electronic)2153-0866


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
Internet address


  • Machine Learning for Robot Control
  • Deep Learning in Grasping and Manipulation
  • Reinforcement Learning
  • Manipulation
  • Control
  • Machine Learning
  • Robot manipulation
  • Deformable objects


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