People prefer interacting with faulty robots significantly more than with those that function and behave flawlessly, a study has found.
Although social robotics is a rapidly advancing field, social robots are not yet at a technical level where they operate without making errors, researchers said.
Nevertheless, most studies in the field are based on the assumption of faultlessly performing robots.
Researchers examined the human interaction partners social signals following a robot error, the team purposefully programmed faulty behaviour into a human-like robots routine and let the participants interact with it.
They measured the robots likability, anthropomorphism and perceived intelligence, and analysed the users reaction when the robot made a mistake.
By means of video coding, researchers could replicate their findings from earlier studies and show that humans respond to faulty robot behaviour with social signals.
Through interviews and user ratings, the team found that erroneous robots were not perceived as significantly less intelligent or anthropomorphic compared to perfectly performing robots.
Instead, although the humans recognised the faulty robots mistakes, they actually rated it as more likeable than its perfectly performing counterpart.
"Our results show that decoding a humans social signals can help the robot understand that there is an error and subsequently react accordingly," said Nicole Mirnig, PhD candidate at University of Salzburg in Austria.
"It lies within the nature of thorough scientific research to pursue a strict code of conduct. 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," Mirnig said.
"We think that because most research focuses on perfect interaction, many potentially crucial aspects are overlooked," she said.
"This finding confirms the Pratfall Effect, which states that peoples attractiveness increases when they make a mistake," Mirnig said.
Specifically exploring erroneous instances of interaction could be useful to further refine the quality of human-robotic interaction.
The study was published in the journal Frontiers in Robotics and AI.