Extraction of Peer-pressure Behaviors that Change Low-moral Behaviors from Large Amount of Data and Its Application to Robots

去年我们更新了数据收集系统，在公共空间收集了更多的数据。通过观察和分析现实世界的数据，我们更好地理解了低道德行为。为了从人体中提取稳定的三维信息，我们更新了系统，开发了一种新的数据提取器。我们从公共场所收集的数据中进行了分析，发现了人类对低道德行为的共同反应。我们从人机交互案例中提取了数十个例子，提出了一种新的机器人模型，称为“暗示性回避”。我们进一步进行了一项实验室研究，以测试新模型如何影响人们。这个模型成功地让人们觉得机器人在结果中更被打扰了。

In the last year we have updated our data collection system and collected more data in the public space. By observing and analyzing the data from the real-world we have better understood the low-moral behaviors. In order to extract stable 3D information from humans, we updated the system by developing a new data extractor.We have done an analysis from the data we have collected in the public space and found a common reaction that humans do to low-moral behaviors. We have extracted tens of examples from human-human interaction cases and proposed a new robot model called “suggestive avoidance”. We further conducted a lab study to test how the new model could influence people. And the model successfully made people perceive the robot as more being bothered in the result.