A Semi-local Surface Feature for Learning Successful Grasping Affordances

Mikkel Tang Thomsen, Dirk Kraft, Norbert Krüger

Abstract

We address the problem of vision based grasp affordance learning and prediction on novel objects by proposing a new semi-local shape-based descriptor, the Sliced Pineapple Grid Feature (SPGF). The primary characteristic of the feature is the ability to encode semantically distinct surface structures, such as “walls”, “edges” and “rims”, that show particular potential as a primer for grasp affordance learning and prediction. When the SPGF feature is used in combination with a probabilistic grasp affordance learning approach, we are able to achieve grasp success-rates of up to 84% for a varied object set of three classes and up to 96% for class specific objects.

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Paper Citation


in Harvard Style

Thomsen M., Kraft D. and Krüger N. (2016). A Semi-local Surface Feature for Learning Successful Grasping Affordances . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 562-572. DOI: 10.5220/0005784505620572


in Bibtex Style

@conference{visapp16,
author={Mikkel Tang Thomsen and Dirk Kraft and Norbert Krüger},
title={A Semi-local Surface Feature for Learning Successful Grasping Affordances},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={562-572},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005784505620572},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - A Semi-local Surface Feature for Learning Successful Grasping Affordances
SN - 978-989-758-175-5
AU - Thomsen M.
AU - Kraft D.
AU - Krüger N.
PY - 2016
SP - 562
EP - 572
DO - 10.5220/0005784505620572