Fitts' List Revisited: An Empirical Study on Function Allocation in a Two-Agent Physical Human-Robot Collaborative Position/Force Task (2505.04722v1)

Published 7 May 2025 in cs.RO and cs.HC

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.

Summary

The paper empirically tests Fitts' List principles to find the optimal allocation of position and force control in human-robot collaboration.
Results indicate the human controlling position and robot force (HR) allocation significantly improves task performance and user acceptance compared to other configurations.
Maintaining human control over position is crucial for user satisfaction, while robotic force control and automated supervision can still enhance efficiency.

An Empirical Study on Function Allocation in Human-Robot Collaboration

The paper "Fitts' List Revisited: An Empirical Study on Function Allocation in a Two-Agent Physical Human-Robot Collaborative Position/Force Task," critically examines the application of Fitts' MABA-MABA principles within physical human-robot collaboration (pHRC) contexts. This investigation offers valuable insights into the optimal distribution of control between humans and robots during collaborative tasks that integrate both position and force elements, with the broader aim of informing developments aligned with Industry 5.0’s human-centric focus.

Study Overview

The researchers conducted an empirical user paper involving 26 participants in a within-subject design to explore various allocations of position and force control in a collaborative task involving an abstract blending activity. Four distinct control allocations were tested: HH (fully manual by humans), HR (human controls position; robot controls force), RH (robot controls position; human controls force), and RR (robot supervises both position and force controls). The paper hypothesized a preference and performance advantage when humans controlled position and robots controlled force, reflecting classic human-machine labor divisions.

Key Findings

Enhanced Performance in HR Allocation: The HR configuration demonstrated a significant improvement in task execution, specifically in minimizing overblending. Participants experienced better physical demand conditions and higher overall system acceptance while reporting greater autonomy, engagement, and reduced frustration. These findings align with the notion that human adaptability and judgment excel in positioning tasks while machines consistently manage force application.
Challenges in RH Allocation: The RH allocation resulted in increased overblending and higher frustration levels, suggesting functional misalignment when robots dictated position movements. This condition likely reduced participants' feelings of competent control, diminishing their engagement and perceived task usefulness.
Acceptance of Automated Collaboration: Interestingly, the RR supervisory condition achieved higher acceptance than RH in terms of user satisfaction and usefulness, despite low autonomy perception. This suggests that while automation can enhance efficiency and reduce physical workload, meaningful human involvement remains crucial to maintaining job satisfaction and engagement.

Implications and Future Work

The paper endorses applying Fitts' principles in the design of collaborative systems in pHRC, advocating for a focus on human positional control complemented by robotic force control to achieve effective and satisfying collaboration. Furthermore, the results imply that for tasks involving both position and force control, maintaining human agency, particularly in task-direction aspects, is vital for psychological satisfaction and motivation.

Future research could expand on these findings by exploring dynamic role allocations in various industrial scenarios and longer-term effects of automated supervisory roles on human autonomy and job satisfaction. Additionally, insights could contribute to more ergonomic and efficient industrial designs that embrace Industry 5.0's human-centric technologies, ensuring technological interventions enhance rather than replace human capabilities.