Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies: Scheduling with Learning Curves

TL;DR

This paper presents a novel scheduling algorithm for human-robot teams that adaptively learns human task proficiencies over time, improving collaboration efficiency and fluency through exploration of human capabilities.

Contribution

It introduces a resource coordination algorithm that models and exploits time-varying human proficiencies, validated through user studies and human-subject experiments.

Findings

Scheduling strategies that explore human capabilities enhance team fluency.

The proposed algorithm efficiently discovers individual human proficiencies.

Exploratory scheduling improves overall team efficiency and collaboration quality.

Abstract

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).