The aging population and chronic staff shortages are prompting care facilities to use service robots (SR) as part of daily care. There are high hopes for support with physical workloads and routine tasks, but adoption often stalls due to technical complexity, poor integration into workflows, and fears that "support" could become "replacement". We address this problem by viewing care as a value-driven practice rather than a list of tasks. In a participatory interview study with caregivers and care recipients in three facilities, based on value-sensitive design, we identified expectations, non-negotiable boundaries, and the values that should guide robot behavior. Participants identified credible roles for SRs in logistics, documentation, reminders, and guidance, but rejected intimate or safety-critical care tasks. Acceptance depends on value-oriented and fluid adaptivity. Robots should dynamically modulate initiative, proactivity, and interaction modality to maintain human attentiveness and warmth, sustain independence, support control over workload, and take legal safeguards into account. We contribute to this (1) with an empirically grounded overview of acceptable potentials and limitations guided by stakeholder values, (2) with a value-sensitive design-based framework for fluid adaptivity as a mechanism that operationalizes values in daily interaction, and (3) design requirements for user-centered, transparent, and context-sensitive SRs that reduce workload and create space for human care rather than replacing it.

Walking in natural environments is widely recognized as an effective stress reduction strategy, often offering greater benefits than walking in built environments. We examined the physiological and cognitive responses to walking in urban forest versus urban built environments in summer and in winter. This study utilized continuous heart rate monitoring with a wearable chest sensor and 2-back cognitive tests. Higher increases in heart rate during the walk and slower post-walk recovery were observed for walks in built environments compared to those in the forest, in both seasons. However, the magnitudes varied between the seasons, emphasizing the contextual nature of restorative benefits. Improvements in the accuracy of the cognitive tests were observed during the forest walks in summer, but the results were less conclusive in winter. Despite these differences, walking in built environments still conferred well-being benefits, supporting stress reduction regardless of the environment or season.

Humor often serves as a catalyst for smoother interpersonal communication, enhancing interaction experience between individuals.While virtual characters can also gain from these benefits, implementing humor naturally in human-character interactions remains an open challenge.In this paper, we propose the Joking and Multimodally Amusing Real-Time Character (J-MARC) system, combining a photorealistic character with advanced large language model (LLM) techniques to contextualize jokes within small talk.In the real-time interaction, the character is able to present the jokes multimodally and to apply nonverbal behavior while listening.

This study investigates whether personalized voice cloning can improve a robot’s likability compared to a design-congruent voice and a distinctly dissimilar voice. Participants interacted with a gender-ambiguous android robot in three different voice conditions. We compared: (1) a personalized voice clone based on the participant’s voice, (2) a design-congruent voice matching the robot’s appearance, and (3) a dissimilar voice, which differs from both the participant’s and the robot’s features.The cloned and design-congruent voices significantly increased likability compared to the dissimilar voice, while anthropomorphism and familiarity showed no significant differences across conditions. Most participants did not immediately recognize their cloned voice until informed that one of the voices was a clone. However, most of the participants were successful when asked to pick out their cloned voice from those used. We assume that voice personalization through similarity to the user improves likability even before the user is aware of this similarity.Our results show that personalized voice cloning is a simple alternative to other methods for the design of robotic voices. It significantly increases robot likability while requiring minimal user effort.