In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camera, and of selecting the most promising one. We demonstrate the effectiveness of our approach on a picking scenario on a real robot platform. Our approach has shown to be more reliable than another recent geometry-based method considered as baseline [7] in terms of grasp stability, by increasing the successful grasp attempts by a factor of six.
@article{arxiv.1907.08088,
title = {Robust and fast generation of top and side grasps for unknown objects},
author = {Brice Denoun and Beatriz Leon and Claudio Zito and Rustam Stolkin and Lorenzo Jamone and Miles Hansard},
journal= {arXiv preprint arXiv:1907.08088},
year = {2019}
}