Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning

Doumanoglou, Andreas; Kargakos, Ανδρέας; Kim, Taekyun; Malassiotis, Sotiris

doi:10.1109/icra.2014.6906974

Cited by 124 publications

(104 citation statements)

References 25 publications

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“…Doumanoglou et al [9] succeeded in recognizing clothing type and shape using a 3D range camera while unfolding. Their framework also provided a next grasping point.…”

Section: Related Workmentioning

confidence: 99%

A method of picking up a folded fabric product by a single-armed robot

et al. 2018

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This paper describes a method to pick up a folded cloth product by a single-armed robot. We focus on a problem on picking up a folded cloth, and organize tasks to attack it. Then, we propose methods of grasp position estimation composed of two stages: detection of the thickest folded hem and pose estimation of the cloth product. In addition, we attempt to search for appropriate grasping postures, and show that there are regions where the success rate of grasp was high. In experiments using an actual robot, we achieved a picking task with 92% success rate.

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“…Doumanoglou et al [9] succeeded in recognizing clothing type and shape using a 3D range camera while unfolding. Their framework also provided a next grasping point.…”

Section: Related Workmentioning

confidence: 99%

A method of picking up a folded fabric product by a single-armed robot

et al. 2018

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“…Research on perception and manipulation tasks for robotic laundry systems consists of: isolating and classifying clothes from a laundry heap [8,5,9], finding a tractable state of the clothes in order to interact and manipulate them [3] and then folding the cloth [1,2,5]. In that regard, Ramisa et al [8] propose a supervised learning approach for grasping highly wrinkled garments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…4(d). 3 In our implementation, σ is 0.8 and the template size is 7×7. Compute the direction of maximal curvature θ using Eq.…”

Section: Clothes Topology and Shape Analysismentioning

confidence: 99%

“…The required perceptual and hand-eye coordination capabilities come naturally to humans, but represent a challenge to current autonomous robotic systems. Although recent research has improved robot capabilities [1][2][3], limitations remain that must be overcome before robots can interact effectively with deformable objects that can be approximated as 2D manifolds in 3D space, such as clothing or textile materials. In this paper, we present a set of novel computer vision algorithms which allow a robot to analyse a deformable clothing, or textile, surface visually and then describe the topology of this surface in order to manipulate it.…”

Section: Introductionmentioning

confidence: 99%

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A Precise Method for Cloth Configuration Parsing Applied to Single-Arm Flattening

Sun

Aragon-Camarasa

Khan

et al. 2016

International Journal of Advanced Robotic Systems

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In this paper, we investigate the contribution that visual perception affords to a robotic manipulation task in which a crumpled garment is flattened by eliminating visually detected wrinkles. In order to explore and validate visually guided clothing manipulation in a repeatable and controlled environment, we have developed a hand-eye interactive virtual robot manipulation system that incorporates a clothing simulator to close the effector-garment-visual sensing interaction loop. We present the technical details and compare the performance of two different methods for detecting, representing and interpreting wrinkles within clothing surfaces captured in high-resolution depth maps. The first method we present relies upon a clustering-based method for localizing and parametrizing wrinkles, while the second method adopts a more advanced geometrybased approach in which shape-topology analysis underpins the identification of the cloth configuration (i.e., maps wrinkles). Having interpreted the state of the cloth configuration by means of either of these methods, a heuristicbased flattening strategy is then executed to infer the appropriate forces, their directions and gripper contact locations that must be applied to the cloth in order to flatten the perceived wrinkles. A greedy approach, which attempts to flatten the largest detected wrinkle for each perception-iteration cycle, has been successfully adopted in this work. We present the results of our heuristic-based flattening methodology which relies upon clustering-based and geometry-based features respectively. Our experiments indicate that geometry-based features have the potential to provide a greater degree of clothing configuration understanding and, as a consequence, improve flattening performance. The results of experiments using a real robot (as opposed to simulated robot) also confirm our proposition that a more effective visual perception system can advance the performance of cloth manipulation.

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“…These mid-level features allowed to significantly improve the classification performance; however, they cannot be automatically computed for a novel test image. Finally, very recently Doumanoglou et al [14], proposed a system for unfolding certain items of clothing that used Random Forests and Hough Forests to determine a grasping point from a canonical view (i.e. the "absolute lowest point" of a hanging garment) using a bimanual manipulator.…”

Section: Garment Manipulationmentioning

confidence: 99%

A 3D descriptor to detect task-oriented grasping points in clothing

Ramisa

Alenyà

Moreno-Noguer

et al. 2016

Pattern Recognition

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Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor.In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.

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Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning

Cited by 124 publications

References 25 publications

A method of picking up a folded fabric product by a single-armed robot

A method of picking up a folded fabric product by a single-armed robot

A Precise Method for Cloth Configuration Parsing Applied to Single-Arm Flattening

A 3D descriptor to detect task-oriented grasping points in clothing

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