MAD-TN：人とロボットの協働における流暢さを測るツール
MAD-TN: A Tool for Measuring Fluency in Human-Robot Collaboration
人間とロボットの協働タスクの流暢さを、時間制約ネットワーク（MAD-TN）でモデル化・測定する手法を提案した論文。
著者: Seth Isaacson, Gretchen Rice, James C. Boerkoel
分類: cs.AI, cs.HC, cs.RO
原文アブストラクト
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.
