Effective grasping enables successful robot-assisted dressing

Advances in computer vision and robotic manipulation are enabling assisted dressing.Peer ReviewedPostprint (author's final draft

New geometric approaches to the singularity analysis of parallel platforms

In general, rearranging the legs of a Stewart- Gough platform, i.e., changing the locations of its leg attachments, modifies the platform singularity locus in a rather unexpected way. Nevertheless, some leg rearrangements have been recently found to leave singularities invariant. In this work, a summary of the some of such singularity-invariant leg rearrangements are presented, and their practical consequences are illustrated with several examples including well-known architectures.Postprint (author’s final draft

Analysing the singularities of 6-SPS parallel robots using virtual legs

A virtual leg in a 6-SPS parallel robot is defined as a leg whose length is determined by the lengths of a subset of the actual legs of the robot. This necessarily implies that this subset of legs defines a rigid subassembly. In this paper, we consider four different rigid subassemblies, and show how the singularities of a robot containing one or several of these subassemblies are modified when substituting its actual legs by virtual legs.Peer Reviewe

Knowledge representation to enable high-level planning in cloth manipulation tasks

Cloth manipulation is very relevant for domestic robotic tasks, but it presents many challenges due to the complexity of representing, recognizing and predicting the behaviour of cloth under manipulation. In this work, we propose a generic, compact and simplified representation of the states of cloth manipulation that allows for representing tasks as sequences of states and transitions semantically. We also define a Cloth Manipulation Graph that encodes all the strategies to accomplish a task. Our novel representation is used to encode two different cloth manipulation tasks, learned from an experiment with human subjects manipulating clothes with video data. We show how our simplified representation allows to obtain a map of meaningful steps that can serve to describe cloth manipulation tasks as domain models in PDDL, enabling high-level planning. Finally, we discuss on the existing skills that could enable the sensory motor grounding and the low-level execution of the plan.Peer ReviewedPostprint (published version

Optimal design of a 6-DOF 4-4 parallel manipulator with uncoupled singularities

A 6-DOF 4-4 parallel manipulator is presented. Its forward kinematics can be solved by a sequence of three trilaterations and, as a consequence, its singularities can be described in geometric terms as the degeneration of three tetrahedra. Moreover, it is shown how the proposed manipulator belongs to the family of flagged parallel manipulators. This identification is useful because the topology of the singularity locus of flagged manipulators has been fully characterized and, what is more important, the singularities of flagged manipulators correspond to uncoupled translations and/or rotations in the workspace of the manipulator. An optimization of its workspace is carried out using Sequential Quadratic Programming and a virtual prototype of the optimal result has been implemented in SolidWorks.Peer Reviewe

Benchmarking cloth manipulation using action graphs: an example in placing flat

Benchmarking robotic manipulation is complex due to the difficulty in reproducing and comparing results across different embodiments and scenarios. Cloth manipula- tion presents additional challenges due to the complex object configuration space. Traditional cloth manipulation papers do not have well defined metrics to evaluate the success of a task or the quality of the result, and are tailored to each evaluation. In this paper we propose to evaluate cloth manipulation seg- menting a task into steps that can be evaluated independently, and to study how their success measures influence in the next segment and relate to task as a whole. In particular, we study a popular task such as placing a cloth flat on a table. We propose a benchmark with simple but continuous evaluation metrics that explore the influence of grasp location into the quality of the task. Our results show that grasp location doesn’t need to be precise on corners, that quality measures focused on evaluating different cloth parts can enlighten issues to solve and that success definition of a segment has to consider its influence on the ability to perform successfully the next segment of action.This work receives funding from the Spanish State Research Agency through the BURG project (CHIST-ERA - PCIN2019-103447) and the Mar ́ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656)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

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

Household cloth object set: fostering benchmarking in deformable object manipulation

© 2022 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 worksBenchmarking of robotic manipulations is one of the open issues in robotic research. An important factor that has enabled progress in this area in the last decade is the existence of common object sets that have been shared among different research groups. However, the existing object sets are very limited when it comes to cloth-like objects that have unique particularities and challenges. This paper is a first step towards the design of a cloth object set to be distributed among research groups from the robotics cloth manipulation community. We present a set of household cloth objects and related tasks that serve to expose the challenges related to gathering such an object set and propose a roadmap to the design of common benchmarks in cloth manipulation tasks, with the intention to set the grounds for a future debate in the community that will be necessary to foster benchmarking for the manipulation of cloth-like objects. Some RGB-D and object scans are collected as examples for the objects in relevant configurations and shared in http://www.iri.upc.edu/groups/perception/ClothObjectSet/Peer ReviewedPostprint (author's final draft

A representation of cloth states based on a derivative of the Gauss linking integral

Robotic manipulation of cloth is a complex task because of the infinite-dimensional shape-state space of textiles, which makes their state estimation very difficult. In this paper we introduce the dGLI Cloth Coordinates, a finite low-dimensional representation of cloth states that allows us to efficiently distinguish a large variety of different folded states, opening the door to efficient learning methods for cloth manipulation planning and control. Our representation is based on a directional derivative of the Gauss Linking Integral and allows us to represent spatial as well as planar folded configurations in a consistent and unified way. The proposed dGLI Cloth Coordinates are shown to be more accurate in the representation of cloth states and significantly more sensitive to changes in grasping affordances than other classic shape distance methods. Finally, we apply our representation to real images of a cloth, showing that with it we can identify the different states using a distance-based classifier.This work was developed under the project CLOTHILDE which has received funding from the European Research Council (ERC) under the EU-Horizon 2020 research and innovation programme (grant agreement No. 741930). M. Alberich-Carramiñana is also with the Barcelona Graduate School of Mathematics (BGSMath) and the Institut de Matemàtiques de la UPC-BarcelonaTech (IMTech), and she and J. Amorós are partially supported by the Spanish State Research Agency AEI/10.13039/501100011033 grant PID2019-103849GB-I00 and by the AGAUR project 2021 SGR 00603 Geometry of Manifolds and Applications, GEOMVAP. J. Borràs is supported by the Spanish State Research Agency MCIN/ AEI /10.13039/501100011033 grant PID2020-118649RB-I00 (CHLOE-GRAPH project).Peer ReviewedPostprint (published version