Inchworm robot grippers for clothes manipulation

Salleh, Khairul Anuar Mohd; Seki, Hiroaki; Kamiya, Yûji; Hikizu, Masatoshi

doi:10.1007/s10015-007-0456-6

Cited by 17 publications

(23 citation statements)

References 5 publications

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“…Common actions on the cloth in such active perception schemes to favor perception include -besides regrasping-shaking for disentangling [25] or for randomizing the item's configuration [26] and rotation at fixed intervals [25,36,26,31]. Such actions can also affect the camera, not only for obtaining stereo from motion [37,38], but just to obtain as much visual data as possible by moving a camera mounted on a robot up and down along the cloth [31] (together with rotation of the cloth, in this reference).…”

Section: Wrinkle Computationmentioning

confidence: 99%

“…Still aiming at detecting a corner while holding the garment piece in the air, the approach followed in [37,38] resorts on a special gripper design, with an integrated seamfollowing mechanism. As for the sensing aspect, besides fototransistor couples at the fingers, whose activation pattern varies according to whether the gripper is holding an edge or a corner, an external 8-bit grayscale CCD camera provides confirmation of corner detection, as well as 3D localization of the corner for grasping, by using motion stereo.…”

Section: Simple Features: Edges and Cornersmentioning

confidence: 99%

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Visual grasp point localization, classification and state recognition in robotic manipulation of cloth: An overview

Jiménez

2017

Robotics and Autonomous Systems

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Cloth manipulation by robots is gaining popularity among researchers because of its relevance, mainly (but not only) in domestic and assistive robotics. The required science and technologies begin to be ripe for the challenges posed by the manipulation of soft materials, and many contributions have appeared in the last years. This survey provides a systematic review of existing techniques for the basic perceptual tasks of grasp point localization, state estimation and classification of cloth items, from the perspective of their manipulation by robots. This choice is grounded on the fact that any manipulative action requires to instruct the robot where to grasp, and most garment handling activities depend on the correct recognition of the type to which the particular cloth item belongs and its state. The high inter-and intraclass variability of garments, the continuous nature of the possible deformations of cloth and the evident difficulties in predicting their localization and extension on the garment piece are challenges that have encouraged the researchers to provide a plethora of methods to confront such problems, with some promising results. The present review constitutes for the first time an effort in furnishing a structured framework of these works, with the aim of helping future contributors to gain both insight and perspective on the subject.

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Section: Wrinkle Computationmentioning

confidence: 99%

Section: Simple Features: Edges and Cornersmentioning

confidence: 99%

Visual grasp point localization, classification and state recognition in robotic manipulation of cloth: An overview

Jiménez

2017

Robotics and Autonomous Systems

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“…This makes the dynamic scenario significantly more challenging to model, and requires a suitable prediction model which is difficult to design due to the high number of degrees of freedom of a flexible object. In [19] the robot spreads a towel by finding and grasping two adjacent corners in the image using vision and two robotic manipulators. The two hands are in constant contact with the cloth, something that cannot always be expected.…”

Section: Related Workmentioning

confidence: 99%

On-line learning of temporal state models for flexible objects

Bergström

Kragić

et al. 2012

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

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Abstract-State estimation and control are intimately related processes in robot handling of flexible and articulated objects. While for rigid objects, we can generate a CAD model beforehand and a state estimation boils down to estimation of pose or velocity of the object, in case of flexible and articulated objects, such as a cloth, the representation of the object's state is heavily dependent on the task and execution. For example, when folding a cloth, the representation will mainly depend on the way the folding is executed.In this paper, we address the problem of learning a temporal object model from observations generated during task execution. We use the case of dynamic cloth folding as a proof-ofconcept for our methodology. In cloth folding, the most important information is contained in the temporal structure of the data requiring appropriate representation of the observations, fast state estimation and a suitable prediction mechanism.Our approach is realized through efficient implementation of feature extraction and a generative process model, exploiting recent hardware advances in conjunction with principled probabilistic models. The model is capable of representing the temporal structure of the data and it is robust to noise in the observations. We present results exploiting our model to classify the success of a folding action.

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“…Researchers focused upon service robot applications have developed systems for grasping clothes [13], [14], folding clothes [15] [16], and tracing edges [17] [18]. Keio University's "Foldy" mobile robot has demonstrated the ability to fold a shirt based on high-level A robotic arm interacts with a pile of unknown objects (laundry) to isolate the individual items one at a time and to learn each object's characteristics.…”

Section: Introductionmentioning

confidence: 99%

Classification of clothing using interactive perception

Willimon

Birchfield

Walker

2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-We present a system for automatically extracting and classifying items in a pile of laundry. Using only visual sensors, the robot identifies and extracts items sequentially from the pile. When an item has been removed and isolated, a model is captured of the shape and appearance of the object, which is then compared against a database of known items. The classification procedure relies upon silhouettes, edges, and other low-level image measurements of the articles of clothing. The contributions of this paper are a novel method for extracting articles of clothing from a pile of laundry and a novel method of classifying clothing using interactive perception. Experiments demonstrate the ability of the system to efficiently classify and label into one of six categories (pants, shorts, short-sleeve shirt, long-sleeve shirt, socks, or underwear). These results show that, on average, classification rates using robot interaction are 59% higher than those that do not use interaction.

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Inchworm robot grippers for clothes manipulation

Cited by 17 publications

References 5 publications

Visual grasp point localization, classification and state recognition in robotic manipulation of cloth: An overview

Visual grasp point localization, classification and state recognition in robotic manipulation of cloth: An overview

On-line learning of temporal state models for flexible objects

Classification of clothing using interactive perception

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