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

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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.