Comparing Models of Disengagement in Individual and Group Interactions
Comparing Models of Disengagement in Individual and Group Interactions
复制标题
个人和群体互动中脱离接触模型的比较
DOI：
10.1145/2696454.2696466
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
B. Scassellati
中科院分区：
文献类型：
--
作者：
Iolanda Leite;Marissa McCoy;D. Ullman;Nicole Salomons;B. Scassellati
交互类型的变化（例如，个人与群体交互）可能会影响为社交机器人开发的数据驱动模型。在本文中，我们提供了第一次调查在数据驱动模型的HRI组大小的变化的影响，通过分析如何从参与者单独互动收集的数据进行训练的模型在测试数据收集组互动，反之亦然。另一个模型结合数据从个人和群体的相互作用也进行了研究。我们在预测儿童与两个社交机器人互动的脱离行为的背景下进行这些实验。我们的研究结果表明，使用组数据训练的模型比其他方法更好地推广到个人参与者。混合模型似乎是一个很好的折衷方案，但它没有达到为特定类型的交互训练的模型的性能水平。
Changes in type of interaction (e.g., individual vs. group interactions) can potentially impact data-driven models developed for social robots. In this paper, we provide a first investigation in the effects of changing group size in datadriven models for HRI, by analyzing how a model trained on data collected from participants interacting individually performs in test data collected from group interactions, and \textit{vice-versa. Another model combining data from both individual and group interactions is also investigated. We perform these experimentsin the context of predicting disengagement behaviors in children interacting with two social robots. Our results show that a model trained with group data generalizes better to individual participants than the other way around. The mixed model seems a good compromise, but it does not achieve the performance levels of the models trained for a specific type of interaction.