aiqbench analysis
This research addresses a critical gap in multi-robot systems by dynamically allocating supervision tasks based on real-time human cognitive states like workload and fatigue. By shifting from fixed capacity assumptions to adaptive regulation, the proposed method prevents operator overload and sustains team performance in high-demand scenarios. The study demonstrates that accounting for human factors is essential for scaling teleoperation teams beyond current limitations.
Real-Time Human-Adaptive Task Allocation for Multi-Human Multi-Robot Supervision
arXiv:2610.00897v1 Announce Type: new Abstract: We propose a human-factor-aware method of allocating robot supervision tasks to multiple human operators. In scenarios where multiple operators occasionally teleoperate multiple robots to help the robots overcome difficulties, the allocation of the supervisory control tasks to humans needs to consider the real-time cognitive states of individual operators. However, most existing methods assume fixed supervisory capacity per operator and overlook fluctuations in the human factors such as workload and fatigue. As a result, workload distribution can be unbalanced where some operators become overloaded while the others remain underused. Our method dynamically regulates supervisory capacity and allocates tasks in a way that maintains balanced mental workload, prevents overload, and improves overall team performance. The allocation method uses a greedy strategy that minimizes estimated operator workloads with task prioritization. Robots are assigned to operators by reflecting their current supervisory capacity where the required effort depends on the types of tasks. In the user study, the analysis across predefined time intervals shows that the proposed method consistently achieves higher performance and lower behavioral signs of fatigue compared to a baseline method that does not consider human factors. These results highlight adaptive capacity adjustment as an effective preventive mechanism for sustaining operator performance in long-duration, high-demand settings.
Abstract:We propose a human-factor-aware method of allocating robot supervision tasks to multiple human operators. In scenarios where multiple operators occasionally teleoperate multiple robots to help the robots overcome difficulties, the allocation of the supervisory control tasks to humans needs to consider the real-time cognitive states of individual operators. However, most existing methods assume fixed supervisory capacity per operator and overlook fluctuations in the human factors such as workload and fatigue. As a result, workload distribution can be unbalanced where some operators become overloaded while the others remain underused. Our method dynamically regulates supervisory capacity and allocates tasks in a way that maintains balanced mental workload, prevents overload, and improves overall team performance. The allocation method uses a greedy strategy that minimizes estimated operator workloads with task prioritization. Robots are assigned to operators by reflecting their current supervisory capacity where the required effort depends on the types of tasks. In the user study, the analysis across predefined time intervals shows that the proposed method consistently achieves higher performance and lower behavioral signs of fatigue compared to a baseline method that does not consider human factors. These results highlight adaptive capacity adjustment as an effective preventive mechanism for sustaining operator performance in long-duration, high-demand settings.
10 pages, 7 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Robotics (cs.RO)
arXiv:2610.00897 [cs.RO]
(or arXiv:2610.00897v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.00897 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 250 words
