Imperial College London
Alternate

ProfessorAndrewDavison

Faculty of Engineering, Department of Computing

Professor of Robot Vision
 
 
 
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Contact

 

+44 (0)20 7594 8316a.davison Website

 
 
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Assistant

 

Ms Lucy Atthis +44 (0)20 7594 8259

 
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Location

 

303William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Johns:2016:10.1109/IROS.2016.7759657,
author = {Johns, E and Leutenegger, S and Davison, AJ},
doi = {10.1109/IROS.2016.7759657},
pages = {4461--4468},
publisher = {IEEE},
title = {Deep learning a grasp function for grasping under gripper pose uncertainty},
url = {http://dx.doi.org/10.1109/IROS.2016.7759657},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - This paper presents a new method for paralleljawgrasping of isolated objects from depth images, underlarge gripper pose uncertainty. Whilst most approaches aimto predict the single best grasp pose from an image, ourmethod first predicts a score for every possible grasp pose,which we denote the grasp function. With this, it is possibleto achieve grasping robust to the gripper’s pose uncertainty,by smoothing the grasp function with the pose uncertaintyfunction. Therefore, if the single best pose is adjacent to aregion of poor grasp quality, that pose will no longer be chosen,and instead a pose will be chosen which is surrounded by aregion of high grasp quality. To learn this function, we traina Convolutional Neural Network which takes as input a singledepth image of an object, and outputs a score for each grasppose across the image. Training data for this is generated byuse of physics simulation and depth image simulation with 3Dobject meshes, to enable acquisition of sufficient data withoutrequiring exhaustive real-world experiments. We evaluate withboth synthetic and real experiments, and show that the learnedgrasp score is more robust to gripper pose uncertainty thanwhen this uncertainty is not accounted for.
AU  - Johns,E
AU  - Leutenegger,S
AU  - Davison,AJ
DO  - 10.1109/IROS.2016.7759657
EP  - 4468
PB  - IEEE
PY  - 2016///
SN  - 2153-0866
SP  - 4461
TI  - Deep learning a grasp function for grasping under gripper pose uncertainty
UR  - http://dx.doi.org/10.1109/IROS.2016.7759657
UR  - https://ieeexplore.ieee.org/document/7759657
UR  - http://hdl.handle.net/10044/1/38815
ER  -
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