Robot navigation plays a critical role in how people perceive, accept, and collaborate with robots in shared environments. This study presents PRoMo (Preference for Robot Motion Questionnaire), a user-centered tool designed to capture human expectations of robot navigation behavior, independent of specific robot forms or tasks. The questionnaire consolidates 28 empirically grounded behaviors into five thematic categories: safety, predictability, proximity, speed and path selection, and responsiveness. Responses from 142 participants reveal strong preferences for navigation strategies that respect personal space, avoid blind spots, and signal awareness through subtle motion cues. Open-ended responses highlight additional concerns, including robot noise and emotional comfort, suggesting that movement is experienced not only as spatial but also as sensory and expressive. Importantly, subjective familiarity with robots showed stronger correlations with behavior preferences than objective experience. These findings provide a generalizable framework for designing socially appropriate robot navigation strategies in human-centered environments. The questionnaire also serves as a practical evaluation tool to guide the development and testing of real-world robot navigation systems.

As society witnesses an increasing presence of robots in domains such as healthcare, education, and service industries, understanding user perceptions and acceptance becomes essential. This research investigates the connection between the perception of robot behavior and user experience, emphasizing the role of social characteristics in shaping perceptions. A sample of 240 participants (mean age 39) evaluated scenarios with non-anthropomorphic robots exhibiting different behavior-one scenario where the robot displayed social behavior (social sensitivity, attention-sharing, and helping) and another where it did not. Insights from the literature underscore the importance of user experience, cultural differences, and prior exposure to robots in shaping attitudes. The present paper replicates the evidence that experience with robots impacts the perception of robots. The novel finding is that users with greater experience prefer robots that show social behavior. The experiments utilized the Mind Attribution Scale, Godspeed Scale, Robotic Social Attributes Scale, and a Prior Experience with Robots Questionnaire. ANCOVA analysis revealed a significant interaction between robot behavior and participants' experience on the perception of the robots. Results indicated that as participants' experience increased, robots with social behavior received higher ratings across all instruments, affirming the impact of personal experiences on shaping perceptions. The study contributes valuable insights into the dynamics of human-robot interaction, guiding the programming of robot behavior for enhanced user experience and societal acceptance in various domains where robots are increasingly present.

In recent years, there has been a growing trend of integrating robots into various dimensions of daily life, to improve user experiences and offer a range of services. This study explores the interactions of robots showing social behavior towards other robots and their impact on human perceptions. We focus on three types of social behavior: social sensitivity, attention-sharing, and helping, aiming to understand how these interactions affect human perceptions of robots. Utilizing Duckiebot mobile robots in carefully crafted experimental setups, participants observed video recordings of these robots’ interactions, which either included or excluded each targeted social behavior. The study measured user responses using established scales such as the Mind Attribution Scale (MAS), the Goodspeed Scale, and the Robotic Social Attributes Scale (RoSAS). The results demonstrated that robots displaying social behavior towards other robots were perceived more positively compared to those that did not exhibit such behavior. Specifically, social sensitivity positively impacted animacy, experience, likability, perceived intelligence, safety, and warmth. Attention-sharing improved perceptions of competence, experience, likability, perceived intelligence, and warmth. Additionally, helping behavior positively affected agency, animacy, anthropomorphism, competence, experience, likability, perceived intelligence, safety, and warmth. This research contributes valuable insights into Human-Robot Interaction (HRI), highlighting the significant impact of robots’ interactions with each other on user experiences and perceptions. The exploration of social behavior lays a foundation for designing robots that evoke positive responses, fostering smoother integration of robotic technology into various aspects of society.