MAD-TN: A Tool for Measuring Fluency in Human-Robot Collaboration
This work addresses the challenge of quantifying coordination in human-robot interaction, which is incremental as it builds on existing daisy-structured networks.
The paper tackles the problem of measuring fluency in human-robot collaboration by proposing the Multi-Agent Daisy Temporal Network (MAD-TN) model, which effectively models collaboration and measures existing fluency metrics while highlighting new metrics hypothesized to correlate with human perception.
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.