Automatic Grasp Pose Generation for Parallel Jaw Grippers

TL;DR

This paper introduces a method for automatically generating diverse and well-distributed grasp poses for parallel jaw grippers on known objects, enhancing robotic grasping capabilities.

Contribution

It presents a clustering-based approach that produces a diverse set of grasp poses, unlike methods focusing solely on optimal or numerous grasps.

Findings

Generated grasps are successfully used in real-world robotic applications.

The method produces a high variance in grasp positions and orientations.

Clustering reduces grasp candidate set while maintaining diversity.

Abstract

This paper presents a novel approach for the automatic offline grasp pose synthesis on known rigid objects for parallel jaw grippers. We use several criteria such as gripper stroke, surface friction, and a collision check to determine suitable 6D grasp poses on an object. In contrast to most available approaches, we neither aim for the best grasp pose nor for as many grasp poses as possible, but for a highly diverse set of grasps distributed all along the object. In order to accomplish this objective, we employ a clustering algorithm to the sampled set of grasps. This allows to simultaneously reduce the set of grasp pose candidates and maintain a high variance in terms of position and orientation between the individual grasps. We demonstrate that the grasps generated by our method can be successfully used in real-world robotic grasping applications.