Homberg, B, Katzschmann, R, Dogar, MR et al. (1 more author) (2015) Haptic identification of objects using a modular soft robotic gripper. In: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 28 Sep - 02 Oct 2015, Hamburg, Germany. IEEE , pp. 1698-1705. ISBN 978-1-4799-9994-1
Abstract
This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements. A highly compliant hand allows for intrinsic robustness to grasping uncertainty, but the specific configuration of the hand and object is not known, leaving undetermined if a grasp was successful in picking up the right object. A soft finger was adapted and combined to form a three finger gripper that can easily be attached to existing robots, for example, to the wrist of the Baxter robot. Resistive bend sensors were added within each finger to provide a configuration estimate sufficient for distinguishing between a set of objects. With one data point from each finger, the object grasped by the gripper can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. This hand is a first step towards robust proprioceptive soft grasping.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015, IEEE. This is an author produced version of a paper published in Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Feb 2016 15:33 |
Last Modified: | 18 Jan 2018 10:15 |
Published Version: | http://dx.doi.org/10.1109/IROS.2015.7353596 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS.2015.7353596 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:95166 |