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MANIPULATION

Dual edge classifier for robust cloth unfolding

Antonio Gabas, Yasuyo Kita, Eiichi Yoshida

Year
2021
Citations
6
Access
Open access

Abstract

Abstract Compared with more rigid objects, clothing items are inherently difficult for robots to recognize and manipulate. We propose a method for detecting how cloth is folded, to facilitate choosing a manipulative action that corresponds to a garment’s shape and position. The proposed method involves classifying the edges and corners of a garment by distinguishing between edges formed by folds and the hem or ragged edge of the cloth. Identifying the type of edges in a corner helps to determinate how the object is folded. This bottom-up approach, together with an active perception system, allows us to select strategies for robotic manipulation. We corroborate the method using a two-armed robot to manipulate towels of different shapes, textures, and sizes.

Keywords

Artificial intelligenceComputer visionRobotComputer scienceEnhanced Data Rates for GSM EvolutionClassifier (UML)Dual (grammatical number)ClothingPosition (finance)Pattern recognition (psychology)

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