论文标题
改革：认识到社会机器人的F形式
REFORM: Recognizing F-formations for Social Robots
论文作者
论文摘要
识别和理解对话群体或F型，对于旨在与人类互动的定位药物来说是一项关键任务。 F形式包含复杂的结构和动态，但在日常面对面的对话中，人们直观地使用了。先前的研究探索识别F形式的方法的研究很大程度上取决于可能无法捕获人类使用的丰富动态行为的启发式算法。我们介绍了改革（通过机器学习识别F型），这是一种数据驱动的方法，用于检测F-formotation的鉴于人类和代理位置和方向。改革将场景分解为所有可能的对，然后通过基于投票的计划重建F形式。我们在三个数据集中评估了我们的方法：SALSA数据集，新近收集的人类数据集以及一套新的ACT人类机器人方案，发现改革对最先进的F-Formation检测算法的准确性提高了。我们还将对称性和紧密度作为定量措施来表征F型。补充视频：https：//youtu.be/fp7etdkkvda，数据集可用：github.com/cu-ironlab/babble
Recognizing and understanding conversational groups, or F-formations, is a critical task for situated agents designed to interact with humans. F-formations contain complex structures and dynamics, yet are used intuitively by people in everyday face-to-face conversations. Prior research exploring ways of identifying F-formations has largely relied on heuristic algorithms that may not capture the rich dynamic behaviors employed by humans. We introduce REFORM (REcognize F-FORmations with Machine learning), a data-driven approach for detecting F-formations given human and agent positions and orientations. REFORM decomposes the scene into all possible pairs and then reconstructs F-formations with a voting-based scheme. We evaluated our approach across three datasets: the SALSA dataset, a newly collected human-only dataset, and a new set of acted human-robot scenarios, and found that REFORM yielded improved accuracy over a state-of-the-art F-formation detection algorithm. We also introduce symmetry and tightness as quantitative measures to characterize F-formations. Supplementary video: https://youtu.be/Fp7ETdkKvdA , Dataset available at: github.com/cu-ironlab/Babble
