Fitts' List Revisited: An Empirical Study on Function Allocation in a Two-Agent Physical Human-Robot Collaborative Position/Force Task

2025-05-07

AI Summary

This study investigates the applicability of classical functional allocation theory—Fitts’ List—to physical human–robot collaboration (pHRC) in Industry 5.0, specifically under the human-centered design principle of “augmenting, not replacing” workers. A within-subjects experiment with 26 participants systematically evaluated four position/force control authority allocation schemes, measuring task performance, cognitive load (NASA-TLX), system acceptance, and perceived autonomy. The study provides the first empirical validation of Fitts’ principles in dynamic pHRC: the “human-led position + robot-led force” allocation emerged as optimal—significantly reducing over-allocation, lowering physical workload, and enhancing perceived autonomy and acceptance. Critically, delegating position control impaired autonomy more severely than delegating force control. These findings offer empirically grounded guidelines for human-centered functional allocation in pHRC systems.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

In this letter, we investigate whether the classical function allocation 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 control) 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.

Problem

• Tests Fitts' List applicability in human-robot physical collaboration
• Evaluates position/force control allocations in human-robot blending tasks
• Examines user experience trade-offs in function delegation autonomy

Innovation

• Human controls position, robot controls force
• Tests Fitts' List in human-robot collaboration
• Empirical study on user experience trade-offs