Automatic grasp planning using shape primitives

TLDR

Automatic grasp planning for robotic hands is difficult because of the vast number of possible hand configurations, yet humans simplify the problem by selecting appropriate prehensile postures for the object and task. The authors model objects with shape primitives (spheres, cylinders, cones, boxes), generate grasp starting positions and pregrasp shapes using rule‑based methods, evaluate them in the GraspIt!

Abstract

Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of rules to generate a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator "GraspIt!", and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm.