Robots assign tasks to humans based on real-time mental workload
Real-Time Human-Adaptive Task Allocation for Multi-Human Multi-Robot Supervision
Robotics
Summary
Controlling many robots by a group of people can be tricky because some people might get too tired or overloaded with work while others have little to do. The authors created a method that watches how tired or busy each person is and then gives tasks to balance the work better. This method helps keep everyone from getting too tired and improves how well the whole team works together. In tests, their method beat a standard approach by keeping people less tired and improving performance over time.
What this means in practice
- •For industrial robot supervisors: Improve allocation of robot teleoperation tasks to multiple operators by balancing their mental workload in real time, reducing operator fatigue and maintaining efficiency.
- •For emergency response teams: Distribute robot supervision tasks dynamically among human operators during high-pressure, multi-robot deployments to sustain operator alertness and task performance.
Authors
Seabin Lee, Sujeong Park, Nayoung Kim, Sungjin Park, Haechan Jung, Changjoo Nam
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.