In public spaces, robots often need to stop suddenly while navigating among pedestrians, such as when they need to make a quick turn or investigate something detected by their sensors. Poorly signaled stops can cause disruptions, forcing pedestrians to make an abrupt avoidance or even lead to risky collisions. To address this issue, we propose a human-inspired stopping model based on the observation of real people's stops. Interestingly, we found that people also often fail to stop without bothering those behind them, causing disruptions. In contrast, we found that stops that do not bother people following closely involve natural signals such as gradual deceleration, head motion, and body orientation changes. These cues help others anticipate upcoming changes, like an upcoming stop. Based on these observations, we implemented our model on a humanoid robot and conducted a field study in a shopping mall. The results show that incorporating these human-like signals leads to smoother interactions with pedestrians following behind, as compared to simple stopping using in standard robot navigation.

We studied how robots could stop people from repeatedly obstructing them by using reactions that people commonly use. From 35 hours of observation of people in a shopping mall, we identified one commonly used reaction, which we named suggestive avoidance. It consists of making a quick movement to the side while rotating the body and gaze toward the obstructing person, in a way that seems to imply that they were bothered by the obstruction. We modeled the human suggestive avoidance behavior, implemented it on a robot, and tested it both in a lab experiment and a field study. The results from the lab study confirmed that people perceive a robot using suggestive avoidance as being more bothered, as well as more human-like. The field study showed that when a robot uses suggestive avoidance people are less likely to bother it again.