基于速度-精度权衡模型的人机协同适应协同槽拟合

T. Petrič, Misel Cevzar, J. Babič
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引用次数: 7

摘要

在物理交互操作任务设置中与其他合作伙伴一起执行任务的好处是什么?通过利用一种新的人类运动学习范式,在这种范式中,两个人相互意识到对方,他们的手通过物体物理连接,我们研究了每个伴侣如何适应他/她的运动行为。我们首先分析了20名受试者在一项任务中的表现,该任务需要将长物体(即管道)操纵成具有不同公差的凹槽。我们在两种不同的情况下测试了执行任务的效率和准确性:a)一个人- 20个受试者;B)两个人合作——十对。我们观察到,在合作操作对象的过程中,任务表现不遵循任何规则,即要么双方都变差,要么双方都变好,要么一方变差。通过利用这一特性,我们提出了一种新的控制算法,用于物理交互和人机合作设置中的机器人,其中机器人适应他/她的伙伴的表现。通过这种方式,它允许人类伙伴提高他/她的任务表现。结果表明,该方法能够成功地适应和匹配人类伴侣的运动,从而使人类伴侣的运动技能得到提高。经过适应,人类与机器人搭档,可以更快地完成任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing speed-accuracy trade-off models for human-robot coadaptation during cooperative groove fitting task
What are the benefits of performing a task with other partners in a physically interactive manipulation task setups? By utilizing a novel human motor learning paradigm, where two individuals are aware of each other and their hands are physically connected through an object, we investigated how each partner adapts his/her motor behavior. We first analyzed performance of twenty subjects on a task where a long object, i.e. a pipe, needs to be manipulated into a groove with different tolerances. We tested efficiency and accuracy of performing the task in two different scenarios: a) one human alone — twenty subjects; b) two humans cooperating — ten pairs. We observed that the task performance during cooperative manipulation of an object does not follow any rules, i.e. either both partners get worse, or both get better, or one partner get and one get worse. By exploiting this properties, we propose a novel control algorithm for robots in physically interactive and cooperative human-robot setups, where the robot adapts to the performance of his/hers partner. This way, it allows the human partner to improve his/hers task performance. The results show that the proposed approach can successfully adapt and match motion of the human partner, and thereby enable the human partner to improve his/her motor skills. After adaption, the human coupled with a robotic partner, can perform the task faster.
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