论文标题
与动态和潜在的人类任务能力的人类机器人团队协调：与学习曲线进行调度
Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies: Scheduling with Learning Curves
论文作者
论文摘要
随着机器人在劳动力中变得无处不在，人类机器人的合作既直观又适应性。机器人的质量根据其明确推理时间变化（即学习曲线）和随机功能的能力而提高，并调整关节工作量以提高效率，同时考虑人类偏好。我们介绍了一种新颖的资源协调算法，该算法使机器人能够通过构建适合随机且随时间变化的人类任务绩效的时间表来探索其人类队友的相对优势和学习能力。我们首先使用从用户研究中收集的数据（n = 20）来验证算法方法，这表明我们可以在发现最新的个人工人熟练程度的同时快速生成和评估强大的时间表。其次，我们进行了一个受试者间实验（n = 90），以验证我们协调算法的功效。人类受试者实验的结果表明，有利于探索的调度策略在提高团队流利度（p = 0.0438）的同时，同时也最大化团队效率（p <0.001），倾向于探索探索的计划往往对人类机器人的协作有益。
As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive. A robot's quality improves based on its ability to explicitly reason about the time-varying (i.e. learning curves) and stochastic capabilities of its human counterparts, and adjust the joint workload to improve efficiency while factoring human preferences. We introduce a novel resource coordination algorithm that enables robots to explore the relative strengths and learning abilities of their human teammates, by constructing schedules that are robust to stochastic and time-varying human task performance. We first validate our algorithmic approach using data we collected from a user study (n = 20), showing we can quickly generate and evaluate a robust schedule while discovering the latest individual worker proficiency. Second, we conduct a between-subjects experiment (n = 90) to validate the efficacy of our coordinating algorithm. Results from the human-subjects experiment indicate that scheduling strategies favoring exploration tend to be beneficial for human-robot collaboration as it improves team fluency (p = 0.0438), while also maximizing team efficiency (p < 0.001).
