Learning from demonstration algorithm for cloth folding manipulator

Sannapaneni, Bharadwaj; Shaswat, Marut; Kumaar, A. A. Nippun

doi:10.1109/icacci.2017.8126035

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…Sannapaneni et al [63] developed an algorithm for learning a folding sequence from the visual detection of special markers attached to key points of the cloth during a demonstration. The learned sequence is then generalized to handle different sizes of clothes which have the same shape as in the demonstration.…”

Section: Planar Objectsmentioning

confidence: 99%

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Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

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This paper aims to provide a comprehensive survey of recent advancements in modelling and autonomous manipulation of non-rigid objects. It first summarizes the recent advances in sensing and modelling of such objects with a focus on describing the methods and technologies used to measure their shape and estimate their material and physical properties. Formal representations considered to predict the deformation resulting from manipulation of non-rigid objects are then investigated. The third part provides a survey of planning and control strategies exploited to operate dexterous robotic systems while performing various tasks on objects made of different non-rigid materials.

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Section: Planar Objectsmentioning

confidence: 99%

“…Sannapaneni et al [63] developed a system that learns a folding task from visual demonstrations. Special markers are attached to key points of the object while it is folded by a human operator.…”

Section: Learned Controlmentioning

confidence: 99%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

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“…Matas [11] shows that even only a handful of human demonstrations is enough to increase the folding performance, however does not disclose how the demonstration data is collected. In [19] users place physical round black objects on the cloths to label the grasp points. Tanaka [18] created a 2D Graphical User Interface (GUI) where a user could choose two grasp points and a vector representing the displacement for a two-arm humanoid robot.…”

Section: B Interfaces For Demonstrating Cloth Foldingmentioning

confidence: 99%

“…Existing data collection methods for human folding demonstrations include selecting grasp points in real images [5], [18] or simulated images [11], providing a sequence of images as sub-goals [16], hard-coded paths pre-defined by humans [17], placing physical markers on clothes [19] and remote teleoperation [20]. Evidenced by the large variety of methods, it is clear that there is no standardization in the literature on how to collect human demonstration data.…”

Section: Introductionmentioning

confidence: 99%

Demonstrating Cloth Folding to Robots: Design and Evaluation of a 2D and a 3D User Interface

Waymouth¹,

Cosgun²,

Newbury³

et al. 2021

Preprint

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An appropriate user interface to collect human demonstration data for deformable object manipulation has been mostly overlooked in the literature. We present an interaction design for demonstrating cloth folding to robots. Users choose pick and place points on the cloth and can preview a visualization of a simulated cloth before real-robot execution. Two interfaces are proposed: A 2D display-and-mouse interface where points are placed by clicking on an image of the cloth, and a 3D Augmented Reality interface where the chosen points are placed by hand gestures. We conduct a user study with 18 participants, in which each user completed two sequential folds to achieve a cloth goal shape. Results show that while both interfaces were acceptable, the 3D interface was found to be more suitable for understanding the task, and the 2D interface suitable for repetition. Results also found that fold previews improve three key metrics: task efficiency, the ability to predict the final shape of the cloth and overall user satisfaction.

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“…Recently, several learning-based approaches to robotic folding were proposed, e.g. [10], [11], [12], [13], [14]. However, these methods have been tested only on a single fabric material and the accuracy has not been measured.…”

Section: Arxiv:190401298v1 [Csro] 2 Apr 2019mentioning

confidence: 99%

Feedback-based Fabric Strip Folding

Petrík

Kyrki

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Accurate manipulation of a deformable body such as a piece of fabric is difficult because of its many degrees of freedom and unobservable properties affecting its dynamics. To alleviate these challenges, we propose the application of feedback-based control to robotic fabric strip folding. The feedback is computed from the low dimensional state extracted from a camera image. We trained the controller using reinforcement learning in simulation which was calibrated to cover the real fabric strip behaviors. The proposed feedback-based folding was experimentally compared to two state-of-the-art folding methods and our method outperformed both of them in terms of accuracy.

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Learning from demonstration algorithm for cloth folding manipulator

Cited by 9 publications

References 11 publications

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

Demonstrating Cloth Folding to Robots: Design and Evaluation of a 2D and a 3D User Interface

Feedback-based Fabric Strip Folding

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