Using a social robot has been proven to have multiple benefits for the training of children with Autism Spectrum Disorder (ASD). However, there is no clarity on the impact of the interaction quality between a child with ASD and a robot on the effectiveness of the therapy. Previous research showed that the use of a robot in Pivotal Response Treatment (PRT) could be an effective treatment component in diminishing ASD-related symptoms. Further analyzing the data from a randomized controlled trial of PRT treatment, we looked at the long-term effects of child–robot game interactions to see whether the interaction quality changes over time. The attention and the engagement of six children were measured through the observation of non-verbal behavior at three different stages in the treatment that took 20 sessions per child. The gaze and arm/hand behavior of the participants towards the robot, the game, and other present humans were observed. The analysis showed no significant decrease in the attention and the engagement of the children towards the robot and the game. However, the attention and engagement toward the parents of the children increased. We conclude that the main result of sustained attention and engagement with the robot is due to the personalization of the games to meet the specific needs of this user group. These specific needs are met through inclusion of variability to the level of development and personal choice of each participating child. We see the additional finding of increased attention towards the parents as especially positive since the children are expected to improve in human–human interaction as a result of this treatment.

Social communication between humans and robots has become critical as a result of the integration of robots into our daily lives as assistants. There is a need to explore how users intuitively understand the behavior of a robot and the impact of social context on that understanding. This study measures mental effort (as indexed by pupil response) and processing time, measured as the time taken to provide the correct answer, to investigate participants’ intuitive understanding of the robot’s gestures. Thirty-two participants participated in a charades game with a TIAGo robot, during which their eyes were tracked. Our findings show a relationship between mental effort and processing time, and indicate that robot gestures, congruence of speech and behavior, and the correctness of interpreting robot behavior influence intuitive understanding. Furthermore, we found that people focused on the robot’s limb movement. Using these findings, we can highlight what features contribute to the intuitive interaction with a robot, thus improving its efficiency.

Expressive robot motion can help establish acceptance of this technology in everyday life, but understanding what makes movement expressive is a complex and multifaceted task. This paper presents the results of an online study with 46 participants, it aims to explore how people perceive and interpret the expressive qualities of human movement and how they envision the translation of their description into an imagined non-humanoid, quadrupedal robot. Through a qualitative analysis of responses, we conceptualize three themes: their understanding of intent, their interpretations of movement qualities, and finally, their translation from human to robot movement. Respondents’ descriptions of their initial understanding of the performer’s intent fall into two modes, bio-mechanical and narrative. We illustrate their interpretations of movement qualities through four strategies: movement features as kinematic indicators, intent indicators, attributed context, and perceived internal states. Lastly, we observe their translation from human to robot movement, with a particular focus on respondents’ use of kinaesthetic empathy and anthropomorphism. Our findings aim to support a bottom-up approach, using users’ general knowledge for designing expressive robot motion.

Robots are becoming part of our social landscape. Social interaction with humans must be efficient and intuitive to understand because nonverbal cues make social interactions between humans and robots more efficient. This study measures mental effort to investigate factors influencing the intuitive understanding of expressive nonverbal robot motions. Using an eye tracker to measure pupil response and gaze, fifty participants were asked to watch eighteen short video clips featuring three different types of robots performing expressive robot behaviors. Our findings indicate that the appearance of the robot, the viewing angle, and the expression shown by the robot all influence the cognitive load. Therefore, they may affect the intuitive understanding of expressive robot behavior. Furthermore, we found differences in the fixation time for different features of the various robots. With these insights, we identified possible improvement directions for making interactions between humans and robots more efficient and intuitive.