An Accessible, Open-Source Dexterity Test: Evaluating the Grasping and Dexterous Manipulation Capabilities of Humans and Robots

Abstract

Evaluating the dexterity of human and robotic hands through appropriate benchmarks, scores, and metrics is of paramount importance for determining how skillful humans are and for designing and developing new bioinspired or even biomimetic end-effectors (e.g., robotic grippers and hands). Dexterity tests have been used in industrial and medical settings to assess how dexterous the hands of workers and surgeons are as well as in robotic rehabilitation settings to determine the improvement or deterioration of the hand function after a stroke or a surgery. In robotics, having a comprehensive dexterity test can allow us to evaluate and compare grippers and hands irrespectively of their design characteristics. However, there is a lack of well defined metrics, benchmarks, and tests that quantify robot dexterity. Previous work has focused on a number of widely accepted functional tests that are used for the evaluation of manual dexterity and human hand function improvement post injury. Each of these tests focuses on a different set of specific tasks and objects. Deriving from these tests, this work proposes a new modular, affordable, accessible, open-source dexterity test for both humans and robots. This test evaluates the grasping and manipulation capabilities by combining the features and best practices of the aforementioned tests, as well as new task categories specifically designed to evaluate dexterous manipulation capabilities. The dexterity test and the accompanying benchmarks allow us to determine the overall hand function recovery and dexterity of robotic end-effectors with ease. More precisely, a dexterity score that ranges from 0 (simplistic, non-dexterous system) to 1 (human-like system) is calculated using the weighted sum of the accuracy and task execution speed subscores. It should also be noted that the dexterity of a robotic system can be evaluated assessing the efficiency of either the robotic hardware, or the robotic perception system, or both. The test and the benchmarks proposed in the study have been validated using extensive human and robot trials. The human trials have been used to determine the baseline scores for the evaluation system. The results show that the time required to complete the tasks reduces significantly with trials indicating a clear learning curve in mastering the dexterous manipulation capabilities associated with the imposed tasks. Finally, the time required to complete the tasks with restricted tactile feedback is significantly higher indicating its importance.