Adapting child-robot interaction to reflect age and gender

Research and commercial robots have infiltrated homes, hospitals and schools, becoming attractive and proving impactful for children’s healthcare, therapy, edutainment, and other applications. The focus of this thesis is to investigate a little explored issue of how children’s perception of the robot changes with age, and thus to create such a robot to adapt to these differences. In particular, this research investigates the impact of gender segregation on children’s interactions with a humanoid NAO robot. To this end, a series of experiments was conducted with children aged between 5 and 12 years old. The results suggest that children aged between 9 and 12 years old do not support gender segregation hypothesis with a gendered robot.In order to dynamically adapt to children’s age and gender, a perception module was developed using depth data and a collected depth dataset of 3D body metrics of 428 children aged between 5 and 16 years old. This module is able to successfully determine children’s gender in real-world settings with 60.89% (76.64% offline) accuracy and estimate children’s age with a mean absolute error of only 1.83 (0.77 offline) years. Additionally, a pretend play testbed was designed in order to address the challenges of evaluating child-robot interaction by exploiting the advantages of multi-modal, multi-sensory perception. The pretend play testbed performed successfully at children’s play center, where a humanoid NAO robot was able to dynamically adapt its gender by changing its synthesized voice to match child’s perceived age and gender. By analyzing the free play of children, the results confirm the hypothesis of gender segregation for children aged younger than 8 years old. These findings are important to consider when designing robotic applications for children in order to improve engagement, which is essential for robot’s educational and therapeutic benefits.