RT Conference Proceedings
T1 Towards clothes hanging via cloth simulation and deep convolutional networks
A1 Estévez Fernández, David
A1 González Víctores, Juan Carlos
A1 Fernández Fernández, Raúl
A1 Balaguer Bernaldo de Quirós, Carlos
AB People spend several hours a week doing laundry, with hanging clothes being one of the laundry tasks to be performed. Nevertheless, deformable object manipulation still proves to be a challenge for most robotic systems, due to the extremely large number of internal degrees of freedom of a piece of clothing and its chaotic nature. This work presents a step towards automated robot clothes hanging by modeling the dynamics of the hanging task via deep convolutional models. Two models are developed to address two different problems: determining if the garment will hang or not (classification), and estimating the future garment location in space (regression). Both models have been trained with a synthetic dataset formed by 15k examples generated though a dynamic simulation of a deformable object. Experiments show that the deep convolutional models presented perform better than a human expert, and that future predictions are largely influenced by time, with uncertainty influencing directly the accuracy of the predictions.
PB Argesim
SN 2305-9974
SN 2306-0271 (online)
YR 2021
FD 2021-03
LK https://hdl.handle.net/10016/34480
UL https://hdl.handle.net/10016/34480
LA eng
NO Proceeding of: 10th EUROSIM Congress on Modelling and Simulation (EUROSIM 2019), Logroño, La Rioja, Spain, July 1-5, 2019
NO This work was supported by RoboCity2030-III-CM project (S2013/MIT-2748), funded by Programas de Actividades I+D in Comunidad de Madrid and EU and by a FPU grant funded by Ministerio de Educación, Cultura y Deporte. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the NVIDIA Titan X GPU used for this research.
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RD 3 may. 2024