A Versatile Gripper for Cloth Manipulation

Donaire, Sònia; Borràs, Júlia Beltrán; Alenyà, Guillem; Torras, Carme

doi:10.1109/lra.2020.3015172

Cited by 25 publications

(14 citation statements)

References 27 publications

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“…An underactuated hand is designed for grasping flat clothes on a table, with a simpler and smarter design. 5/ Donaire et al, 2020 Besides the open-close feature of the thumb and finger, the thumb tip has a wide base plane and variable friction surfaces.…”

Section: / Koustoumpardis Et Al 2014mentioning

confidence: 99%

A systematic automated grasping approach for automatic manipulation of fabric with soft robot grippers

Liu

Jian-jun

et al. 2023

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Purpose The garment industry will be one of the major beneficiaries of advances in smart manufacturing, as it is highly labor-intensive and heavily depends on labor force. Manipulating robots in human environments has made great strides in recent years. However, the main research has focused on rigid, solid objects and core capabilities such as grasping, placing remain a challenging problem when dealing with soft textiles. The experimental results indicate that adopting the proposed bionic soft finger will provide garment manufacturers with smart manufacturing capabilities. Then, the purpose of this paper is to utilize the flexibility of the soft finger to transfer fabric layer by layer without damage in garment automation. Design/methodology/approach In this paper, a new way to separate layer by layer pieces of fabric has been inspired by the rise of soft robotics and their applications in automation. Fabric gripping is accomplished by wiping deformation and pinching the fabric. A single fabric piece is separated from cutting pile by the soft finger in four steps: making an arch by pressing, wiping deformation, grasping and separating, and placing. Findings The case study demonstrated that the soft finger arrangement for automated grasping of fabric pieces of a garment can be successfully applied to delicate fabric. A combination of cloth shape and weight determines the number of soft fingers. In addition, the soft finger was tested on different types of fabrics to determine its performance and application capabilities. The technology may be used to produce clothing intelligently in the future, such as intelligent stacking, intelligent transportation and intelligent packaging, to increase clothing industry productivity. Originality/value An industrial bionic soft finger gripping system is proposed in this paper for application in the field of fabric automatic manipulation. A piece of fabric could be picked up and released layer by layer from a stack by the proposed gripper without creating any damage to it. Soft grippers have the right proportion of softness and rigidity like a human being. A soft finger has a potential affinity for soft materials such as fabrics without damaging either their surface or their properties.

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Section: / Koustoumpardis Et Al 2014mentioning

confidence: 99%

A systematic automated grasping approach for automatic manipulation of fabric with soft robot grippers

Liu

Jian-jun

et al. 2023

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“…However, it is difficult to realize automatic operation due to the characteristics of flaky deformable object manipulation, such as a thin, large flat area and changeable shape (Jiménez, 2017). Therefore, the research on the operation of end effector with flaky deformable object manipulation is still a frontier problem in the field of robot technology (Donaire et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Koustoumpardis et al (2018) grabbed the fabric by driving three fingers through the cam mechanism, which can increase the grabbing reliability and efficiency compared with the traditional two-finger gripper. The multifunctional gripper designed by Donaire et al (2020) can pick and fold clothes by changing the friction force of the contact surface and adjusting the degree of freedom of the gripper. Yamazaki and Abe (2021) realized the grabbing and releasing of cotton fabric through two sets of cylindrical brushes.…”

Section: Introductionmentioning

confidence: 99%

A versatile end effector for grabbing and spreading of flaky deformable object manipulation

Yuan

Ren

et al. 2023

Mech. Sci.

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Abstract. It is a conundrum to use robots to complete the intelligent operation of leather and clothing fabrics; this is mainly reflected in the design of an end effector that can realize this function. We have determined the design requirements and objectives of the end effector by observing the action of manually grabbing and spreading leather, and put forward an end effector composed of a reconfigurable multi-link mechanism and roller-type fingertip, which can be used to grab and spread flaky deformable object manipulation. At the same time, the reconfigurable multi-link mechanism and roller-type fingertip are introduced and analyzed in detail. By setting up the prototype of the end effector and completing the grabbing experiment of three kinds of soft pieces, we verified and tested the rationality of the prototype design. The result shows that the grabbing success rate will be affected by many factors, and further exploration is needed to further improve the performance of the end effector.

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“…5(c). Instead of designing a complex manipulator [10] or a versatile gripper [11] to directly have contact with clothes based on real-time measurements on the highly deformable textiles, we aim to solve clothes folding by using this assistive tool to construct limited and unified manipulation primitives, which allows us to only estimate few intermediate states, instead of measuring deformations frequently. Fig.…”

Section: Introductionmentioning

confidence: 99%

Learning Cloth Folding Tasks with Refined Flow Based Spatio-Temporal Graphs

Zhou,

Zahra,

Duan

et al. 2021

Preprint

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Cloth folding is a widespread domestic task that is seemingly performed by humans but which is highly challenging for autonomous robots to execute due to the highly deformable nature of textiles; It is hard to engineer and learn manipulation pipelines to efficiently execute it. In this paper, we propose a new solution for robotic cloth folding (using a standard folding board) via learning from demonstrations. Our demonstration video encoding is based on a high-level abstraction, namely, a refined optical flow-based spatiotemporal graph, as opposed to a low-level encoding such as image pixels. By constructing a new spatiotemporal graph with an advanced visual corresponding descriptor, the policy learning can focus on key points and relations with a 3D spatial configuration, which allows to quickly generalize across different environments. To further boost the policy searching, we combine optical flow and static motion saliency maps to discriminate the dominant motions for better handling the system dynamics in real-time, which aligns with the attentional motion mechanism that dominates the human imitation process. To validate the proposed approach, we analyze the manual folding procedure and developed a custom-made endeffector to efficiently interact with the folding board. Multiple experiments on a real robotic platform were conducted to validate the effectiveness and robustness of the proposed method.

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A Versatile Gripper for Cloth Manipulation

Cited by 25 publications

References 27 publications

A systematic automated grasping approach for automatic manipulation of fabric with soft robot grippers

A systematic automated grasping approach for automatic manipulation of fabric with soft robot grippers

A versatile end effector for grabbing and spreading of flaky deformable object manipulation

Learning Cloth Folding Tasks with Refined Flow Based Spatio-Temporal Graphs

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