With the rapid advancement of robotics, robots' helping behaviors are increasingly framed not only as functional assistance but also as prosocially meaningful interaction. In this context, the resource cost borne by the help-provider is a critical factor, yet it has not been systematically explored in existing human-robot interaction (HRI) research. Understanding how humans perceive and respond to different types of helping is essential for building better human-robot relationships. This study addresses this gap through two experiments. Study 1 examined the role of agent resource type (robot's own resources vs. external resources). Results showed that when robots shared their own resources, participants did not report significant differences in overall attitudes or prosocial behavior, but attributed higher performance trust and expressed stronger feelings of gratitude and guilt. Study 2 further examined the importance of agent resources (robot battery level: high vs. low). The results showed that even when relative costs were the same, participants tended to perceive sharing from a low-battery robot as more reliable, while variations in resource type or importance did not significantly change social responses. These findings suggest that human evaluations of robots are shaped not only by the outcomes of helping but also by the perceived cost and sacrifice underlying robot actions. Our work offers an initial direction for integrating resource cost considerations into the design of social robots.
Robophobia is a recent and growing trend on social media, where users create humorous videos that play on the general fear of robots. While framed as jokes, these videos contribute to the public's attitudes and expectations of robots, influencing what is socially and culturally acceptable. To investigate this emerging trend, we conducted a thematic analysis of 200 English-speaking TikTok videos using online ethnography to explore how users engage with robophobia and what implications this may have for the Human-Robot Interaction (HRI) field. Our findings show that robophobia on TikTok is predominantly expressed through humorous skits and verbal abuse of real-world robots in public spaces. Common themes include fears about humans in romantic relationships with robots, and frequent use of derogatory terms such as "clanker" to verbally "dehumanize" robots. The robophobia trend is culturally embedded and reveals people's underlying attitudes and fears toward robots in society, both now and in the future. We discuss the implications of these findings for the field of Human-Robot Interaction (HRI), emphasizing how public expectations are shaped by cultural narratives, and stress the need for culturally sensitive, expectation-aware HRI research and robot design.
Children's attention during shared reading is fragile, yet most child-robot reading systems are reactive: they wait for visible lapses before intervening. We present a physiology-driven pipeline that anticipates near-term disengagement and adjusts delivery before attention is lost. A wrist-worn sensor streams EDA and HRV; short-window features feed a lightweight classifier that estimates the probability of disengagement 5-10 s ahead. When the probability exceeds a threshold and guardrails are satisfied, a bounded adaptation policy triggers subtle, delivery-only changes-slower pacing, clause-aligned pauses, and light prosodic emphasis-without altering script content. In a within-subjects study with 36 children (ages 3-6) reading with a Luka robot, the adaptive condition reduced disengagement events by about half (IRR0.49), improved comprehension (+0.86 points, especially sequencing/inference questions), and was preferred by most children (61%). The results show that anticipatory, physiology-aware control can stabilize attention and yield measurable learning benefits while preserving transparency and safety via simple rules. We discuss design implications for low-overhead sensing, auditable policies, and child-appropriate guardrails, and we release implementation details to support reproducibility.
Social robots can support children's emotional skills development through playful interactions, yet skills like emotional self-disclosure remain underexplored. This study investigates the impact of a social robot designed to encourage emotional self-disclosure during an emotion-identification game with children aged 6-10. In a between subjects design with 28 participants across two local schools, we compared a Reflective condition, where the robot actively encouraged emotional self-disclosure through question-asking and reciprocal sharing, to a Control condition, where the robot did not. Children in the Reflective condition engaged in emotional self-disclosure when prompted, and showed higher engagement than those in Control. Direct question-asking was more effective than reciprocal self-disclosure. Results suggested that children who perceived the robot as kinder disclosed more, whereas those who viewed it as more real disclosed less. These findings highlight the potential of social robots to foster emotional skills in children and inform the design of future child-robot interaction research. CCS Concepts center dot Human-centered computing -> User studies.
In-home service robots embodied as mobile platforms with onboard cameras are increasingly being proposed for well-being monitoring and fall detection for older adults. Yet, how users perceive a robot's movement and observation behavior within the home remains underexplored. This work examines user preferences for robot-based observation strategies in Human Activity Recognition (HAR) and evaluates how these strategies affect recognition performance. In a within-subject study with adults over 50, we compare stationary versus adaptive-distance observation behaviors. Results reveal that while stationary observation is generally preferred for being less intrusive, preferences vary depending on the activity context. HAR accuracy remains comparable across both strategies, and combining robot and ambient sensing enhances recognition of complex, temporally extended activities.
We introduce a real-time system for tracking hand pose using 6axis inertial measurement units (IMUs) without requiring magnetometers or external sensors. Accurate hand pose tracking with only 6-axis IMUs is known to be fundamentally challenging due to the absence of a shared heading reference, leading to severe drift and inter-sensor misalignment. To overcome these limitations, we propose a hybrid method that combines a learning-based pose estimation approach followed by a late-stage Extended Kalman Filter (EKF). The learning-based model estimates noisy yet reasonable hand poses and is trained with drift-insensitive features like gravity vectors and wrist-relative gyroscope signals. On the other hand the EKF can appropriately filter the noise from pose estimates leading to robust tracking. Evaluated on a 12-hour dataset spanning 23 interaction tasks across 10 participants, our system improves joint angle accuracy by 40% over an EKF-only baseline and by 18% over a learning-only approach, achieving a mean joint error below 10 degrees. The resulting framework enables real-time hand tracking invariant to magnetic perturbations, occlusion, or lighting changes, and is well suited for robotics, human-robot interaction (HRI), and human-computer interaction (HCI) applications.
