Integrating domestic robots into everyday life requires not only reliable execution but also prior alignment of task goals between humans and robots. While prior research has examined input interfaces and feedback strategies, it has largely focused on objective performance metrics and often overlooked user variability. To address this gap, we conducted a survey study with 113 participants across four groups: adolescents, younger adults, older adults, and wheelchair users. The survey captured participants’ expectations of future robots (roles, embodiments, and concerns) and their preferences for instruction delivery and robot feedback before execution. Our results reveal both shared and group-specific patterns. Across groups, participants prioritized efficiency in instruction delivery and reliability in robot feedback. Regarding group differences: adolescents emphasized efficiency, wheelchair users valued transparency, and older adults may benefit from additional explanations of novel interaction technologies. Based on these findings, we derive stage-aware, context-sensitive, and group-adaptive design principles and recommendations to guide future robot interfaces.

While voice-controlled automated Wheelchair-mounted robotic arms (WMRAs) could potentially offer more natural and simplified interactions than manual control, they introduce new challenges related to human-robot collaboration. To explore user expectations in terms of functionality and interface, we conducted semi-structured interviews with 13 powered wheelchair users who have upper limb impairments. A prototype with a robotic arm, a camera, and a Unity-based simulated interface was developed to help users understand the concept and functionality of the voice-controlled automated WMRA system during interviews. With safety as a priority, we found that users prioritized the WMRA's ability to grasp objects in challenging positions, such as on the ground or at high locations, and emphasized the need for a versatile gripper. Users also expected the WMRA to assist in performing complex daily tasks, with varying expectations for its performance based on task difficulty. Regarding the interface, users sought more information about the system's awareness and task execution, while emphasizing the importance of avoiding information overload. The demand for detailed safety information, such as temperature and gripping force, pointed to the need for enhanced sensor capabilities in the WMRA system. Additionally, concerns about privacy underscored the need for clear communication on privacy policies. Our results provide user-centered insights for automated WMRA, offering design implications and future research directions in areas such as user modeling, hardware, algorithms, and interface development.