Deep Learning a Grasp Function for Grasping Under Gripper Pose Uncertainty

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Title: Deep Learning a Grasp Function for Grasping Under Gripper Pose Uncertainty
Authors: Johns, E
Leutenegger, S
Davison, AJ
Item Type: Conference Paper
Abstract: This paper presents a new method for paralleljaw grasping of isolated objects from depth images, under large gripper pose uncertainty. Whilst most approaches aim to predict the single best grasp pose from an image, our method first predicts a score for every possible grasp pose, which we denote the grasp function. With this, it is possible to achieve grasping robust to the gripper’s pose uncertainty, by smoothing the grasp function with the pose uncertainty function. Therefore, if the single best pose is adjacent to a region of poor grasp quality, that pose will no longer be chosen, and instead a pose will be chosen which is surrounded by a region of high grasp quality. To learn this function, we train a Convolutional Neural Network which takes as input a single depth image of an object, and outputs a score for each grasp pose across the image. Training data for this is generated by use of physics simulation and depth image simulation with 3D object meshes, to enable acquisition of sufficient data without requiring exhaustive real-world experiments. We evaluate with both synthetic and real experiments, and show that the learned grasp score is more robust to gripper pose uncertainty than when this uncertainty is not accounted for.
Issue Date: 1-Dec-2016
Date of Acceptance: 5-Aug-2016
URI: http://hdl.handle.net/10044/1/38815
DOI: https://dx.doi.org/10.1109/IROS.2016.7759657
ISSN: 2153-0866
Publisher: IEEE
Journal / Book Title: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
Copyright Statement: © 2016 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.
Sponsor/Funder: Dyson Technology Limited
Funder's Grant Number: PO 4500378543
Conference Name: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Robotics
Computer Science
cs.RO
cs.CV
cs.LG
Publication Status: Published
Start Date: 2016-10-09
Finish Date: 2016-10-14
Conference Place: Daejeon, Korea
Open Access location: http://www.doc.ic.ac.uk/~ejohns/Documents/ejohns-grasp-function-iros2016.pdf
Appears in Collections:Faculty of Engineering
Computing



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