3,419 research outputs found
A Grasping-centered Analysis for Cloth Manipulation
Compliant and soft hands have gained a lot of attention in the past decade
because of their ability to adapt to the shape of the objects, increasing their
effectiveness for grasping. However, when it comes to grasping highly flexible
objects such as textiles, we face the dual problem: it is the object that will
adapt to the shape of the hand or gripper. In this context, the classic grasp
analysis or grasping taxonomies are not suitable for describing textile objects
grasps. This work proposes a novel definition of textile object grasps that
abstracts from the robotic embodiment or hand shape and recovers concepts from
the early neuroscience literature on hand prehension skills. This framework
enables us to identify what grasps have been used in literature until now to
perform robotic cloth manipulation, and allows for a precise definition of all
the tasks that have been tackled in terms of manipulation primitives based on
regrasps. In addition, we also review what grippers have been used. Our
analysis shows how the vast majority of cloth manipulations have relied only on
one type of grasp, and at the same time we identify several tasks that need
more variety of grasp types to be executed successfully. Our framework is
generic, provides a classification of cloth manipulation primitives and can
inspire gripper design and benchmark construction for cloth manipulation.Comment: 13 pages, 4 figures, 4 tables. Accepted for publication at IEEE
Transactions on Robotic
A 3D descriptor to detect task-oriented grasping points in clothing
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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.Peer ReviewedPostprint (author's final draft
G.O.G: A Versatile Gripper-On-Gripper Design for Bimanual Cloth Manipulation with a Single Robotic Arm
The manipulation of garments poses research challenges due to their
deformable nature and the extensive variability in shapes and sizes. Despite
numerous attempts by researchers to address these via approaches involving
robot perception and control, there has been a relatively limited interest in
resolving it through the co-development of robot hardware. Consequently, the
majority of studies employ off-the-shelf grippers in conjunction with dual
robot arms to enable bimanual manipulation and high dexterity. However, this
dual-arm system increases the overall cost of the robotic system as well as its
control complexity in order to tackle robot collisions and other robot
coordination issues. As an alternative approach, we propose to enable bimanual
cloth manipulation using a single robot arm via novel end effector design --
sharing dexterity skills between manipulator and gripper rather than relying
entirely on robot arm coordination. To this end, we introduce a new gripper,
called G.O.G., based on a gripper-on-gripper structure where the first gripper
independently regulates the span, up to 500mm, between its fingers which are in
turn also grippers. These finger grippers consist of a variable friction module
that enables two grasping modes: firm and sliding grasps. Household item and
cloth object benchmarks are employed to evaluate the performance of the
proposed design, encompassing both experiments on the gripper design itself and
on cloth manipulation. Experimental results demonstrate the potential of the
introduced ideas to undertake a range of bimanual cloth manipulation tasks with
a single robot arm. Supplementary material is available at
https://sites.google.com/view/gripperongripper.Comment: Accepted for IEEE Robotics and Automation Letters in January 2024.
Dongmyoung Lee and Wei Chen contributed equally to this researc
Effective grasping enables successful robot-assisted dressing
Advances in computer vision and robotic manipulation are enabling assisted dressing.Peer ReviewedPostprint (author's final draft
Cloth manipulation and perception competition
In the last decade, several competitions in robotic manipulation have been organised as a way to drive scientific progress in the field. They enable comparison of different approaches through a well-defined benchmark with equal test conditions. However, current competitions usually focus on rigid-object manipulation, leaving behind the challenges that suppose grasping deformable objects, especially highly-deformable ones as cloth-like objects. In this paper, we want to present the first competition in perception and manipulation of textile objects as an eficient method to accelerate scientific progress in the domain of domestic service robots. To do so, we selected a small set of tasks to benchmark in a common framework using the same set of objects and assessment methods. This competition has been conceived to freely distribute the Household Cloth Object Set to research groups working on cloth manipulation and perception and participate on the challenge. In this work, we present an overview of the tasks that are proposed in the competition, detailed descriptions of the tasks and more information on the scoring and rules are provided in the website http://www.iri.upc.edu/groups/perception/ClothManipulationChallenge/Peer ReviewedPostprint (published version
Learning to Singulate Layers of Cloth using Tactile Feedback
Robotic manipulation of cloth has applications ranging from fabrics
manufacturing to handling blankets and laundry. Cloth manipulation is
challenging for robots largely due to their high degrees of freedom, complex
dynamics, and severe self-occlusions when in folded or crumpled configurations.
Prior work on robotic manipulation of cloth relies primarily on vision sensors
alone, which may pose challenges for fine-grained manipulation tasks such as
grasping a desired number of cloth layers from a stack of cloth. In this paper,
we propose to use tactile sensing for cloth manipulation; we attach a tactile
sensor (ReSkin) to one of the two fingertips of a Franka robot and train a
classifier to determine whether the robot is grasping a specific number of
cloth layers. During test-time experiments, the robot uses this classifier as
part of its policy to grasp one or two cloth layers using tactile feedback to
determine suitable grasping points. Experimental results over 180 physical
trials suggest that the proposed method outperforms baselines that do not use
tactile feedback and has better generalization to unseen cloth compared to
methods that use image classifiers. Code, data, and videos are available at
https://sites.google.com/view/reskin-cloth.Comment: IROS 2022. See https://sites.google.com/view/reskin-cloth for
supplementary materia
Benchmarking bimanual cloth manipulation
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cloth manipulation is a challenging task that, despite its importance, has received relatively little attention compared to rigid object manipulation. In this paper, we provide three benchmarks for evaluation and comparison of different approaches towards three basic tasks in cloth manipulation: spreading a tablecloth over a table, folding a towel, and dressing. The tasks can be executed on any bimanual robotic platform and the objects involved in the tasks are standardized and easy to acquire. We provide several complexity levels for each task, and describe the quality measures to evaluate task execution. Furthermore, we provide baseline solutions for all the tasks and evaluate them according to the proposed metrics.Peer ReviewedPostprint (author's final draft
A virtual reality framework for fast dataset creation applied to cloth manipulation with automatic semantic labelling
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Teaching complex manipulation skills, such as folding garments, to a bi-manual robot is a very challenging task, which is often tackled through learning from demonstration. The few datasets of garment-folding demonstrations available nowadays to the robotics research community have been either gathered from human demonstrations or generated through simulation. The former have the great difficulty of perceiving both cloth state and human action as well as transferring them to the dynamic control of the robot, while the latter require coding human motion into the simulator in open loop, i.e., without incorporating the visual feedback naturally used by people, resulting in far-from-realistic movements. In this article, we present an accurate dataset of human cloth folding demonstrations. The dataset is collected through our novel virtual reality (VR) framework, based on Unity’s 3D platform and the use of an HTC Vive Pro system. The framework is capable of simulating realistic garments while allowing users to interact with them in real time through handheld controllers. By doing so, and thanks to the immersive experience, our framework permits exploiting human visual feedback in the demonstrations while at the same time getting rid of the difficulties of capturing the state of cloth, thus simplifying data acquisition and resulting in more realistic demonstrations. We create and make public a dataset of cloth manipulation sequences, whose cloth states are semantically labeled in an automatic way by using a novel low-dimensional cloth representation that yields a very good separation between different cloth configurations.The research leading to these results receives funding from the European Research Council (ERC) from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and project SoftEnable (HORIZONCL4-2021-DIGITAL-EMERGING-01-101070600). Authors also received funding from project CHLOE-GRAPH (PID2020-118649RB-I00) funded by MCIN/ AEI /10.13039/501100011033 and COHERENT (PCI2020-120718-2) funded by MCIN/ AEI /10.13039/501100011033 and cofunded by the ”European Union NextGenerationEU/PRTR”.Peer ReviewedPostprint (author's final draft
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