Learning Visual Feedback Control for Dynamic Cloth Folding

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

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu


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
OtsikkoProceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
ISBN (elektroninen)978-1-6654-7927-1
DOI - pysyväislinkit
TilaJulkaistu - 26 jouluk. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE/RSJ International Conference on Intelligent Robots and Systems - Kyoto, Japani
Kesto: 23 lokak. 202227 marrask. 2022


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems


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