Real-Time Human-Adaptive Task Allocation for Multi-Human Multi-Robot Supervision

2026-09-30

AI Summary

This study addresses the challenge that neglecting operators’ real-time cognitive states in multi-robot supervision often leads to workload imbalance and degraded performance. To overcome this, we propose a human-factors-aware dynamic task allocation framework that departs from conventional fixed-capacity assumptions by introducing an adaptive capacity regulation mechanism driven by real-time cognitive load. By integrating a greedy strategy that minimizes estimated workload with task prioritization and a capacity-aware allocation algorithm, the framework achieves dynamically balanced task scheduling. A user study demonstrates that, compared to baseline approaches, the proposed method significantly enhances overall team performance, effectively mitigates behavioral indicators of fatigue, prevents cognitive overload, and sustains efficient long-term operations.

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.

Problem

• task allocation
• multi-human multi-robot
• cognitive workload
• human factors
• supervisory control

Innovation

• Human-adaptive task allocation
• Cognitive workload balancing
• Multi-human multi-robot supervision
• Dynamic supervisory capacity
• Greedy strategy