Grasping Benchmarks: Normalizing for Object Size \& Approximating Hand Workspaces

TL;DR

This paper proposes a standardized, functional measurement procedure for quantifying robotic hand workspace sizes, enabling consistent communication of grasping capabilities across diverse hand designs.

Contribution

It introduces a generalizable, functional approach to measure and compare robotic hand workspace sizes based on grasp scenarios, applicable to various hand configurations.

Findings

Demonstrated measurements on seven different hand configurations.

Provided a reproducible measurement procedure and supplementary resources in a GitHub repository.

Facilitates better scientific communication of grasping capabilities.

Abstract

The varied landscape of robotic hand designs makes it difficult to set a standard for how to measure hand size and to communicate the size of objects it can grasp. Defining consistent workspace measurements would greatly assist scientific communication in robotic grasping research because it would allow researchers to 1) quantitatively communicate an object's relative size to a hand's and 2) approximate a functional subspace of a hand's kinematic workspace in a human-readable way. The goal of this paper is to specify a measurement procedure that quantitatively captures a hand's workspace size for both a precision and power grasp. This measurement procedure uses a {\em functional} approach -- based on a generic grasping scenario of a hypothetical object -- in order to make the procedure as generalizable and repeatable as possible, regardless of the actual hand design. This functional approach lets the measurer choose the exact finger configurations and contact points that satisfy the generic grasping scenario, while ensuring that the measurements are {\em functionally} comparable. We demonstrate these functional measurements on seven hand configurations. Additional hand measurements and instructions are provided in a GitHub Repository.