Social robots offer a promising avenue for boosting engagement and education in pediatric clinical settings. This paper evaluates the suitability of Mirrly, a novel custom-built tabletop social robot, by evaluating children’s opinions of it through the application area of amblyopia ("lazy eye") treatment. We conducted focus group sessions with 24 children (ages 7–10 years) to gauge initial impressions of Mirrly as a health literacy tool and interactive game facilitator. Preliminary results indicate that children perceive the robot as highly knowledgeable (M = 4.696/5) and likeable (M = 4.160/5). Immediately following the interaction, 100% of participants retained key information about vision-saving treatment (eye patching) presented during the study. We discuss these findings and children’s suggestions for improving the robot’s design in the context of child-robot healthcare interactions. We provide insights for future in-clinic deployments aimed at supporting patient well-being and health outcomes for children diagnosed with amblyopia.

Trust is a critical factor in human–robot interaction. This study investigates how using human data to train reinforcement learning (RL) agents affects perceived performance and trust. We conducted two experiments: an online study using the video game Seaquest, and a physical robot study involving a wire loop task. In both, RL agents were trained using demonstrations from human users with varying skill levels, as well as without human data. Participants then rated the agents’ performance and how much they trusted them. Our results for the online study indicate that RL agents trained with expert human data received significantly higher trust ratings than other agents and that trust was higher in replay and long training conditions compared to short and no training. Videos showing beginner players were also trusted, despite lower performance, although these differences did not always reach statistical significance. This highlights that trust is influenced by factors beyond mere performance, suggesting a role for cues such as perceived human involvement. In the physical robot study, our findings indicate that trust was significantly influenced by the type of training data, with data from an experienced human having a positive effect under certain training conditions. Performance ratings were influenced primarily by the training approach, with replay receiving higher ratings than short training. Although trust was notably higher under certain conditions, performance ratings did not always align with trust, indicating a partial dissociation between the two measures. Together, these results suggest that training with high-quality human data can enhance the trustworthiness of RL agents under appropriate training conditions. However, trust depends not only on performance but also on how human-like an agent’s behavior appears. Designing agents for trust-sensitive applications may therefore benefit from aligning behavior with user expectations rather than optimizing performance alone.

People with motor disabilities of neuromuscular origin often struggle with operating the computer through a commercially available mouse and QWERTY keyboard. This work presents an alternative mouse and keyboard interface, which is personalizable to meet the individual needs of the target population to allow a more efficient use of the computer. The regular/commercially available mouse is therefor replaced by a spectacle frame equipped with pressure sensors for mouse click activation and an inertial measurement unit for mouse cursor control through head movements. The alternative keyboard is based around a ten button keyboard, with multiple letters assigned to each key. The users can then chose the desired word from a list of word suggestions that is compiled based on the keyboard input. Prior experiments, the users went through a software guided calibration procedure to experimentally determine individual thresholds and parameters. The system was tested by two nondisabled participants through typing and click tests, followed by a questionnaire to give feedback on the system.

In human communication, backchanneling is an important part of the natural interaction protocol. The purpose is to signify the listener's attention, understanding, agreement, or to indicate that a speaker should go on talking. While the effects of backchanneling robots on humans have been investigated, studies of how and when humans backchannel to talking robots is poorly studied. In this paper we investigate how the robot's behavior as a speaker affects a human listener's backchanneling behavior. This is interesting in Human-Robot Interaction since backchanneling between humans has been shown to support more fluid interactions, and human-robot interaction would therefore benefit from mimicking this human communication feature. The results show that backchanneling increases when the robot exhibits backchannel-inviting cues such as pauses and gestures. Furthermore, clear differences between how a human backchannels to another human and to a robot are shown.