Abstract
This paper presents a method for the robotic unfolding of garments from random initial configurations. In order to handle various types of garments and fabrics, features common in all garments are explored and identified, while manipulations mimicking the human unfolding movements are derived by a learning procedure. The proposed approach involves the transformation of the garment from random to half-folded state so that its configuration can be analyzed through a visual hierarchical architecture. Then, the two layers of the garment and the folding axis that unites them are extracted. The upper layer is finally unfolded by the application of a path extracted based on learning by demonstration. The proposed framework is experimentally evaluated on a variety of garments and the produced results indicate the feasibility of the method.
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Triantafyllou, D., Aspragathos, N. (2022). Garment Type Agnostic Robotic Unfolding of Garments from Random Configuration. In: Müller, A., Brandstötter, M. (eds) Advances in Service and Industrial Robotics. RAAD 2022. Mechanisms and Machine Science, vol 120. Springer, Cham. https://doi.org/10.1007/978-3-031-04870-8_57
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DOI: https://doi.org/10.1007/978-3-031-04870-8_57
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