Predicting Positions of People in Human-Robot Conversational Groups
Predicting Positions of People in Human-Robot Conversational Groups
复制标题
预测人机对话组中人的位置
DOI：
10.5555/3523760.3523815
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
D. Szafir
中科院分区：
文献类型：
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作者：
Hooman Hedayati;D. Szafir
在社交环境中运行的机器人必须能够识别、理解和推理人类会话组（即，F-编队）。虽然已经开发了几种算法来识别这样的组，但是很少有关于机器人如何在组分类之后推理不准确性的研究（例如，仅识别5个组成员中的4个）。我们通过数据驱动的方法来解决这一差距，建立人类群体定位的知识。通过分析多个会话组数据集，我们已经开发了一个系统，用于识别高概率区域，该区域指示人们相对于单个锚参与者可能站在组中的区域。我们使用这些区域的知识来训练两个模型，并在社交机器人上实现。第一模型可以估计部分观察到的会话组的真实大小（即，仅检测到部分参与者的组）。我们的第二个模型可以预测任何未被发现的参与者可能居住的位置。总之，这些模型可以通过增加对噪声输入数据的鲁棒性来改进F-编队检测算法。
Robots that operate in social settings must be able to recognize, understand, and reason about human conversational groups (i.e., F-formations). While several algorithms have been developed for identifying such groups, there has been little research on how robots might reason about inaccuracies following group classification (e.g., recognizing only 4 of 5 group members). We address this gap through a data-driven approach that builds knowledge of human group positioning. By analyzing multiple conversational group data sets, we have developed a system for identifying high probability regions that indicate areas where people are likely to stand in a group relative to a single anchor participant. We use knowledge of these regions to train two models, which we implement on a social robot. The first model can estimate the true size of a partially-observed conversational group (i.e., a group where only some of the participants were detected). Our second model can predict the locations where any undetected participants are likely to reside. Together, these mod-els may improve F-formation detection algorithms by increasing robustness to noisy input data.