A Planar-Symmetric SO(3) Representation for Learning Grasp Detection

TL;DR

This paper introduces a new SO(3) representation tailored for planar-symmetric robotic hands, improving the consistency and accuracy of neural-network-based grasp detection in both simulated and real-world settings.

Contribution

It presents a novel SO(3) parametrization for symmetric poses using the 2D Bingham distribution, enhancing grasp detection performance.

Findings

Improved grasp detection accuracy in simulation and real-world tests.

More consistent rotation outputs for planar-symmetric hands.

Effective handling of symmetry-induced ambiguities in SO(3) representations.

Abstract

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.