Fluency is an important metric in Human-Robot Interaction (HRI) that describes the coordination with which humans and robots collaborate on a task. Fluency is inherently linked to the timing of the task, making temporal constraint networks a promising way to model and measure fluency. We show that the Multi-Agent Daisy Temporal Network (MAD-TN) formulation, which expands on an existing concept of daisy-structured networks, is both an effective model of human-robot collaboration and a natural way to measure a number of existing fluency metrics. The MAD-TN model highlights new metrics that we hypothesize will strongly correlate with human teammates' perception of fluency.
@article{arxiv.1909.06675,
title = {MAD-TN: A Tool for Measuring Fluency in Human-Robot Collaboration},
author = {Seth Isaacson and Gretchen Rice and James C. Boerkoel},
journal= {arXiv preprint arXiv:1909.06675},
year = {2020}
}