Elangovan Nathan, Chang Che-Ming, Gao Geng, Liarokapis Minas
New Dexterity Research Group, Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland, New Zealand.
Front Robot AI. 2022 Apr 25;9:808154. doi: 10.3389/frobt.2022.808154. eCollection 2022.
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.
通过适当的基准、分数和指标来评估人类和机器人手的灵巧性，对于确定人类的技能水平以及设计和开发新的受生物启发甚至仿生的末端执行器（如机器人夹具和手）至关重要。灵巧性测试已在工业和医疗环境中用于评估工人和外科医生手部的灵巧程度，也在机器人康复环境中用于确定中风或手术后手部功能的改善或恶化情况。在机器人技术中，拥有一个全面的灵巧性测试可以让我们无论夹具和手的设计特点如何，都能对其进行评估和比较。然而，缺乏明确界定的量化机器人灵巧性的指标、基准和测试。先前的工作集中在一些广泛接受的功能测试上，这些测试用于评估手动灵巧性和受伤后人类手部功能的改善情况。这些测试中的每一个都侧重于不同的特定任务和对象集。基于这些测试，这项工作提出了一种新的模块化、经济实惠、易于使用、开源的人类和机器人灵巧性测试。该测试通过结合上述测试的特点和最佳实践，以及专门设计用于评估灵巧操作能力的新任务类别，来评估抓取和操作能力。灵巧性测试及相关基准使我们能够轻松确定机器人末端执行器的整体手部功能恢复情况和灵巧性。更确切地说，使用准确性和任务执行速度子分数的加权总和来计算一个从0（简单、不灵巧的系统）到1（类人系统）的灵巧性分数。还应注意的是，可以通过评估机器人硬件或机器人感知系统或两者的效率来评估机器人系统的灵巧性。该研究中提出的测试和基准已通过广泛的人类和机器人试验得到验证。人类试验用于确定评估系统的基线分数。结果表明，随着试验次数的增加，完成任务所需的时间显著减少，这表明在掌握与规定任务相关的灵巧操作能力方面存在明显的学习曲线。最后，在触觉反馈受限的情况下完成任务所需的时间明显更长，这表明了触觉反馈的重要性。
