To effectively interact with humans in crowded social settings, such as malls, hospitals, and other public spaces, robots should be able to actively participate in both group and one-to-one interactions. Most existing robots, however, have been found to perform much better when communicating with individual users than with groups of conversing humans.
Hooman Hedayati and Daniel Szafir, two researchers at University of North Carolina at Chapel Hill, have recently developed a new data-driven technique that could improve how robots communicate with groups of humans. This method, presented in a paper presented at the 2022 ACM/IEEE International Conference on Human-Robot Interaction (HRI ‘22), allows robots to predict the positions of humans in conversational groups, so that they do not mistakenly ignore a person when their sensors are fully or partly obstructed.
“Being in a conversational group is easy for humans but challenging for robots,” Hooman Hedayati, one of the researchers who carried out the study, told TechXplore. “Imagine that you are talking with a group of friends, and whenever one of your friends blinks, she stops talking and asks if you are still there. This potentially annoying scenario is roughly what can happen when a robot is in conversational groups.”
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