Modeling Human Temporal Uncertainty in Human-Agent Teams
This addresses the challenge of improving human-robot fluency in scheduling, though it is incremental as it builds on existing automated scheduling methods.
The paper tackled the problem of representing human timing uncertainty in automated scheduling for human-robot teams by developing an online collaborative game to model this uncertainty from crowd-workers, finding that heavy-tailed distributions, particularly Log-Normal, best fit the data.
Automated scheduling is potentially a very useful tool for facilitating efficient, intuitive interactions between a robot and a human teammate. However, a current gapin automated scheduling is that it is not well understood how to best represent the timing uncertainty that human teammates introduce. This paper attempts to address this gap by designing an online human-robot collaborative packaging game that we use to build a model of human timing uncertainty from a population of crowd-workers. We conclude that heavy-tailed distributions are the best models of human temporal uncertainty, with a Log-Normal distribution achieving the best fit to our experimental data. We discuss how these results along with our collaborative online game will inform and facilitate future explorations into scheduling for improved human-robot fluency.