A method for handling a specific part of clothing by dual arms

Kita, Yoshihiro; Ueshiba, Toshio; Neo, Ee Sian; Kita, Naoki

doi:10.1109/iros.2009.5354213

Cited by 34 publications

(31 citation statements)

References 8 publications

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“…The authors tested their approach on seven articles of clothing of the end-to-end approach and were successful with 20 out of 30 trials. Our work differs from the previous papers [6,[11][12][13][14] in that we work with a single manipulator and our results do not depend on hanging our clothing in the air by two grasp points. Our approach is independent of the type or how many manipulators that are used to collect the data.…”

Section: Previous Workmentioning

confidence: 57%

“…The work in [6] provides a three-step process to successfully find a grasp point that the humanoid robot can grab: (1) clothing state estimation (previous work [4]); (2) calculation of the theoretically optimal position and orientation of the hand for the next action; and (3) modification of position and orientation, based on the robots limitations, so the action can be executed. The 3D model is used by calculating the orientation of the shirt model at the grasp point.…”

Section: Previous Workmentioning

confidence: 99%

“…Our approach is independent of the type or how many manipulators that are used to collect the data. Our work also differs from that of [6,13,14] in that we do not require or use predefined shape models. We instead use novel characteristic features that describe each category of clothing.…”

Section: Previous Workmentioning

confidence: 99%

“…Figure 1 gives a high-level flow chart of these various steps. In the past, several bodies of work have attempted at solving the tasks of handling [1][2][3][4][5][6][7][8], separating/isolating [8][9][10][11][12], classifying [6,9,[11][12][13][14][15], unfolding/flattening [14,16], and folding [17] clothes. Figure 1 gives a flow chart of the various areas.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work on classifying clothing [6,[11][12][13][14] used dual manipulators to freely hang each article of clothing at two grasp points to reveal the overall shape of each article of clothing. In contrast to previous work, our approach is tested on an unorganized, unflattened article for the purpose of classifying each individual piece of clothing.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Classification of Clothing Using Midlevel Layers

2013

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We present a multilayer approach to classify articles of clothing within a pile of laundry. The classification features are composed of color, texture, shape, and edge information from 2D and 3D data within a local and global perspective. The contribution of this paper is a novel approach of classification termed L-M-H, more specifically LC-S-H for clothing classification. The multilayer approach compartmentalizes the problem into a high (H) layer, multiple midlevel (characteristics (C), selection masks (S)) layers, and a low (L) layer. This approach produces "local" solutions to solve the global classification problem. Experiments demonstrate the ability of the system to efficiently classify each article of clothing into one of seven categories (pants, shorts, shirts, socks, dresses, cloths, or jackets). The results presented in this paper show that, on average, the classification rates improve by +27.47% for three categories , +17.90% for four categories, and +10.35% for seven categories over the baseline system, using SVMs (Chang and Lin, 2001).

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Section: Previous Workmentioning

confidence: 57%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Clothing Using Midlevel Layers

2013

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Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making

Seita

Jamali

Laskey

et al. 2022

Springer Proceedings in Advanced Robotics

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Efficiently Learning Single-Arm Fling Motions to Smooth Garments

Chen

Huang

Novoseller

et al. 2023

Springer Proceedings in Advanced Robotics

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Many fabric handling and 2D deformable material tasks in homes and industry require singulating layers of material such as opening a bag or arranging garments for sewing. In contrast to methods requiring specialized sensing or end effectors, we use only visual observations with ordinary parallel jaw grippers. We propose SLIP: Singulating Layers using Interactive Perception, and apply SLIP to the task of autonomous bagging. We develop SLIP-Bagging, a bagging algorithm that manipulates a plastic or fabric bag from an unstructured state, and uses SLIP to grasp the top layer of the bag to open it for object insertion. In physical experiments, a YuMi robot achieves a success rate of 67% to 81% across bags of a variety of materials, shapes, and sizes, significantly improving in success rate and generality over prior work. Experiments also suggest that SLIP can be applied to tasks such as singulating layers of folded cloth and garments. Supplementary material is available at https://sites.google.com/view/slip-bagging/.

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A method for handling a specific part of clothing by dual arms

Cited by 34 publications

References 8 publications

Classification of Clothing Using Midlevel Layers

Classification of Clothing Using Midlevel Layers

Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making

Efficiently Learning Single-Arm Fling Motions to Smooth Garments

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