智能分配机器人故障给最合适的操作员，提升协作效率。
Human-in-the-Loop Failure Recovery with Adaptive Task Allocation
- 根据操作员能力动态分配故障任务
- 减少机器人空闲时间，提升系统整体表现
- 适合需要人机协同的医疗助手机器人场景
自新冠疫情以来，具备更高自主性的移动操作机器人和人形助手机器人越来越多地被用于患者护理与生活协助。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，常需人类干预以恢复故障。高效的人机协作对确保机器人能从最胜任的操作员处获得帮助至关重要，有助于减轻机器人负担并减少任务中断。本文提出一种自适应故障分配方法（ARFA），通过建模操作员能力，并基于其实际表现持续更新信念。针对每次故障，奖励函数综合评估操作员能力、历史数据、任务紧迫性及当前工作负载，将故障分配给预期收益最高的操作员。仿真与用户研究均表明，ARFA优于随机分配，显著降低机器人空闲时间，提升系统性能，并实现更均衡的操作员工作负载分布。
原文摘要 · Abstract (English)
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.
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