Utilizing speed-accuracy trade-off models for human-robot coadaptation during cooperative groove fitting task

Abstract

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.