  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).
