Humans like faulty robots more than flawless ones

Humans prefer interacting with faulty robots significantly more than with robots that function and behave flawlessly, new research has found. For the study, published in the journal Frontiers in Robotics and AI, researchers examined how people react to robots that exhibit faulty behaviour compared to perfectly performing robots.

"Our results showed that the participants liked the faulty robot significantly more than the flawless one. This finding confirms the Pratfall Effect, which states that people's attractiveness increases when they make a mistake," said corresponding author Nicole Mirnig from University of Salzburg, Austria.

Although social robotics is a rapidly advancing field, social robots are not yet at a technical level where they operate without making errors. Nevertheless, most studies in the field are based on the assumption of faultlessly performing robots."Alternatives resulting from unforeseeable conditions that develop during an experiment are often not further regarded or simply excluded," Mirnig said.

"However, we suppose that faulty instances of human-robot interaction are full with knowledge that can help us further improve the interactional quality in new dimensions. We think that because most research focuses on perfect interaction, many potentially crucial aspects are overlooked," Mirnig added.

To examine the human interaction partners' social signals following a robot error, the research team purposefully programmed faulty behaviour into a human-like robot's routine and let the participants interact with it. They measured the robot's likability, anthropomorphism and perceived intelligence, and analysed the users' reaction when the robot made a mistake.

The research team found that somewhat surprisingly, erroneous robots were not perceived as significantly less intelligent or anthropomorphic compared to perfectly performing robots. Instead, although the humans recognised the faulty robot's mistakes, they actually rated it as more likeable than its perfectly performing counterpart.

These findings may have exciting implications for the field of social robotics, since they emphasise the importance for robot creators to keep potential imperfections in mind when designing robots.