Empathy and Guilt as Mechanisms for Help-Seeking in Human–Humanoid Interaction

The development of humanoid robots has experienced a sudden acceleration during the last years, due to the large advancements made in actuation technology, generative AI and computer vision. The design of humanoid robots makes them useful in scenarios where many different tasks must be achieved, and humans are present. Furthermore, their resemblance to humans opens new ways of communication when compared to traditional robots. However, humanoid robots may find themselves in a situation where human assistance is required, e.g. due to limitations in their sensing and movement capabilities. As such, different help-seeking strategies and their effectiveness need to be explored. This article compares the effect of inducing empathy and guilt in humans as means to request help after a mistake made by a robot. An in the wild experiment conducted between subjects was performed in the University of Southern Denmark (SDU) with a total of 123 participants across 3 scenarios of help-seeking strategies, described as: distressed, sarcastic, and neutral. The results showed a statistical difference between the strategies, proving that using the concepts of empathy and guilt elicitation with robots has the potential to improve human-humanoid collaboration.