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

Visuotactile Affordances for Cloth Manipulation with Local Control

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time. See http://nehasunil.com/visuotactile/visuotactile.html for videos.Comment: Accepted at CoRL 2022. Project website: http://nehasunil.com/visuotactile/visuotactile.htm

The Canon, Spring 1978: Volume 8, Number 3

Cover Design by Pat Leegwater Frontispiece by Margariete Timmermans This is for Pop-Bottle Pete by Dave Groenenboom Navajo by Karen Kole Keep Silence by Judy Van Gorp Rev. 3:15,16 by Sandra Elgersma [photo of windmill] by Dan Zinkand Three summers ago... by Bonnie Kuipers Godfried\u27s Anointment by Marj De Bruyn Raindrop by Greta Vanderhoek Storageus by Harry J. Kits Verne Meyer by Editorial Staff [photo of girls] by Dave Groenenboom Words by Margariete Timmermans Words flow by Dave Groenenboom [drawing of cigarette] by Gerry Vaandering The last cigarette... by Dave Groenenboom No gentle china... by Bonnie Kuipers [drawing of chairs] by Dan Van Heyst Of Farrah and Chaucer by Kathy Teune [drawing of rodent] by Dave Mulder The Dressmaker by Pat Boonstra Mere Foolishness by Dianne Vander Hoek [drawing of tree] by Margariete Timmermans A Friday Night\u27s Choice by The Weaz On Whether Pigs Have Wings by Pat Leegwater The Sietze Buning Phenomenon by Editorial Staff Parole by Geraldine de Rooy Lord by Lavonne Nannenga C 160 by Margariete Timmermans Between Heaven & Hell: The Grand Canyon by Neil Culbertson I Am a Woman by Bonnie Kuipers I smell microscopic life... by Neil Culbertsonhttps://digitalcollections.dordt.edu/dordt_canon/1070/thumbnail.jp

The Teenager's Problem: Efficient Garment Decluttering With Grasp Optimization

This paper addresses the ''Teenager's Problem'': efficiently removing scattered garments from a planar surface. As grasping and transporting individual garments is highly inefficient, we propose analytical policies to select grasp locations for multiple garments using an overhead camera. Two classes of methods are considered: depth-based, which use overhead depth data to find efficient grasps, and segment-based, which use segmentation on the RGB overhead image (without requiring any depth data); grasp efficiency is measured by Objects per Transport, which denotes the average number of objects removed per trip to the laundry basket. Experiments suggest that both depth- and segment-based methods easily reduce Objects per Transport (OpT) by $20\%$; furthermore, these approaches complement each other, with combined hybrid methods yielding improvements of $34\%$. Finally, a method employing consolidation (with segmentation) is considered, which manipulates the garments on the work surface to increase OpT; this yields an improvement of $67\%$ over the baseline, though at a cost of additional physical actions