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Main Authors: Ko, Tianyi, Ikeda, Takuya, Sato, Hiroya, Nishiwaki, Koichi
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2410.04826
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author Ko, Tianyi
Ikeda, Takuya
Sato, Hiroya
Nishiwaki, Koichi
author_facet Ko, Tianyi
Ikeda, Takuya
Sato, Hiroya
Nishiwaki, Koichi
contents Planar-symmetric hands, such as parallel grippers, are widely adopted in both research and industrial fields. Their symmetry, however, introduces ambiguity and discontinuity in the SO(3) representation, which hinders both the training and inference of neural-network-based grasp detectors. We propose a novel SO(3) representation that can parametrize a pair of planar-symmetric poses with a single parameter set by leveraging the 2D Bingham distribution. We also detail a grasp detector based on our representation, which provides a more consistent rotation output. An intensive evaluation with multiple grippers and objects in both the simulation and the real world quantitatively shows our approach's contribution.
format Preprint
id arxiv_https___arxiv_org_abs_2410_04826
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Planar-Symmetric SO(3) Representation for Learning Grasp Detection
Ko, Tianyi
Ikeda, Takuya
Sato, Hiroya
Nishiwaki, Koichi
Robotics
Planar-symmetric hands, such as parallel grippers, are widely adopted in both research and industrial fields. Their symmetry, however, introduces ambiguity and discontinuity in the SO(3) representation, which hinders both the training and inference of neural-network-based grasp detectors. We propose a novel SO(3) representation that can parametrize a pair of planar-symmetric poses with a single parameter set by leveraging the 2D Bingham distribution. We also detail a grasp detector based on our representation, which provides a more consistent rotation output. An intensive evaluation with multiple grippers and objects in both the simulation and the real world quantitatively shows our approach's contribution.
title A Planar-Symmetric SO(3) Representation for Learning Grasp Detection
topic Robotics
url https://arxiv.org/abs/2410.04826