Planning High-Quality Grasps using Mean Curvature Object Skeletons

TL;DR

This paper introduces a grasp planning method that combines object skeletons and local surface analysis to generate robust, high-quality grasps for robotic hands, effective across various objects and resilient to positioning errors.

Contribution

The novel integration of mean curvature skeletons with local surface analysis for autonomous grasp planning on diverse objects.

Findings

Capable of generating high-quality grasps autonomously.

Effective across a wide variety of object models.

Robust to hand positioning errors in real-world scenarios.

Abstract

In this work, we present a grasp planner which integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object model is incorporated by building the mean curvature skeleton and segmenting the object accordingly in order to identify object regions which are suitable for applying a grasp. Second, the local surface structure is investigated to construct feasible and robust grasping poses by aligning the hand according to the local object shape. We show how this information can be used to derive different grasping strategies, which also allows to distinguish between precision and power grasps. We applied the approach to a wide variety of object models of the KIT and the YCB real-world object model databases and evaluated the approach with several robotic hands. The results show that the skeleton-based grasp planner is capable to autonomously generate high-quality grasps in an efficient manner. In addition, we evaluate how robust the planned grasps are against hand positioning errors as they occur in real-world applications due to perception and actuation inaccuracies. The evaluation shows that the majority of the generated grasps are of high quality since they can be successfully applied even when the hand is not exactly positioned.