Towards clothes hanging via cloth simulation and deep convolutional networks

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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.
Proceeding of: 10th EUROSIM Congress on Modelling and Simulation (EUROSIM 2019), Logroño, La Rioja, Spain, July 1-5, 2019
Robotics, Deformable objects, Laundry, Deep learning
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Balaguer Bernaldo de Quirós, Carlos; González Victores, Juan Carlos; Estévez Fernández, David; Fernández Fernández, Raúl. Towards clothes hanging via cloth simulation and deep convolutional networks. In: Simulation notes Europe: journal on developments and trends in modelling and simulation, 31(3) (Selected EUROSIM 2019 Postconf. Publ.), March 2021, Pp. 169-176