In this letter, we investigate whether classical function allocation-the principle of assigning tasks to either a human or a machine-holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This study empirically tests the applicability of Fitts' List within physical Human-Robot Collaboration, by conducting a user study (N=26, within-subject design) to evaluate four distinct allocations of position/force control between human and robot in an abstract blending task. We hypothesize that the function in which humans control the position achieves better performance and receives higher user ratings. When allocating position control to the human and force control to the robot, compared to the opposite case, we observed a significant improvement in preventing overblending. This was also perceived better in terms of physical demand and overall system acceptance, while participants experienced greater autonomy, more engagement and less frustration. An interesting insight was that the supervisory role (when the robot controls both position and force) was rated second best in terms of subjective acceptance. Another surprising insight was that if position control was delegated to the robot, the participants perceived much lower autonomy than when the force control was delegated to the robot. These findings empirically support applying Fitts' principles to static function allocation for physical collaboration, while also revealing important nuanced user experience trade-offs, particularly regarding perceived autonomy when delegating position control.

翻译：本文旨在探讨经典功能分配原则——即将任务分配给人类或机器的准则——是否适用于物理人机协作领域，这对于为工业5.0提供如何最佳增强而非取代工人的见解至关重要。本研究通过开展用户实验（N=26，被试内设计），评估在抽象混合任务中人与机器人之间四种不同的位置/力控制分配方式，实证检验了菲茨列表在物理人机协作中的适用性。我们假设由人类控制位置的功能能实现更优性能并获得更高的用户评分。当将位置控制分配给人类、力控制分配给机器人时，与相反分配方式相比，我们观察到在防止过度混合方面有显著改善。该分配方式在体力需求和整体系统接受度方面也被感知为更优，同时参与者体验到更高的自主性、更强的参与感和更低的挫败感。一个有趣的发现是，监督角色（当机器人同时控制位置和力时）在主观接受度方面被评为第二佳。另一令人惊讶的发现是，若将位置控制委托给机器人，参与者感知到的自主性远低于将力控制委托给机器人的情况。这些发现从实证角度支持将菲茨原则应用于物理协作的静态功能分配，同时也揭示了重要的用户体验细微权衡，特别是在委托位置控制时的感知自主性方面。