Since the recent Covid-19 pandemic, mobile manipulators and humanoid assistive robots with higher levels of autonomy have increasingly been adopted for patient care and living assistance. Despite advancements in autonomy, these robots often struggle to perform reliably in dynamic and unstructured environments and require human intervention to recover from failures. Effective human-robot collaboration is essential to enable robots to receive assistance from the most competent operator, in order to reduce their workload and minimize disruptions in task execution. In this paper, we propose an adaptive method for allocating robotic failures to human operators (ARFA). Our proposed approach models the capabilities of human operators, and continuously updates these beliefs based on their actual performance for failure recovery. For every failure to be resolved, a reward function calculates expected outcomes based on operator capabilities and historical data, task urgency, and current workload distribution. The failure is then assigned to the operator with the highest expected reward. Our simulations and user studies show that ARFA outperforms random allocation, significantly reducing robot idle time, improving overall system performance, and leading to a more distributed workload among operators.

翻译：自近期新冠疫情爆发以来，具有更高自主水平的移动操作机器人与仿人辅助机器人日益广泛地应用于病患护理与生活辅助领域。尽管自主性不断提升，这些机器人在动态非结构化环境中仍难以可靠运行，常需人工干预以从故障中恢复。有效的人机协作对于使机器人能够获得最适格操作员的协助至关重要，以此降低操作员工作负荷并最小化任务执行中断。本文提出一种面向人类操作员的机器人故障自适应分配方法（ARFA）。该方法对人类操作员的能力进行建模，并依据其实际故障恢复表现持续更新这些能力评估。针对每个待解决的故障，奖励函数将基于操作员能力与历史数据、任务紧急度及当前工作负荷分布计算预期收益。故障随后将被分配给具有最高预期收益的操作员。我们的仿真与用户研究表明，ARFA方法在随机分配基准上表现出显著优势，能够有效减少机器人闲置时间，提升整体系统性能，并实现更均衡的操作员工作负荷分布。