Expressive robot motion can help establish acceptance of this technology in everyday life, but understanding what makes movement expressive is a complex and multifaceted task. This paper presents the results of an online study with 46 participants, it aims to explore how people perceive and interpret the expressive qualities of human movement and how they envision the translation of their description into an imagined non-humanoid, quadrupedal robot. Through a qualitative analysis of responses, we conceptualize three themes: their understanding of intent, their interpretations of movement qualities, and finally, their translation from human to robot movement. Respondents’ descriptions of their initial understanding of the performer’s intent fall into two modes, bio-mechanical and narrative. We illustrate their interpretations of movement qualities through four strategies: movement features as kinematic indicators, intent indicators, attributed context, and perceived internal states. Lastly, we observe their translation from human to robot movement, with a particular focus on respondents’ use of kinaesthetic empathy and anthropomorphism. Our findings aim to support a bottom-up approach, using users’ general knowledge for designing expressive robot motion.

Successfully implementing robots to support senior adults requires their acceptance. Leveraging nonverbal communication could enhance the ease and intuitiveness of accepting robot assistance. However, it is essential to see how different age groups understand nonverbal communication cues to understand the dynamics between different user groups and assistive robots. Our research specifically delves into the intuitive understanding of handshaking gestures across multiple interactions, focusing on seniors (between 70 and 97) and young (21 and 26) adults. Through a combination of observations and open-ended surveys, we conducted a video observation and thematic analysis. Interestingly, our findings indicate no significant differences between the two age groups, except for reactions and interaction time variables. Furthermore, we report on possible motivations behind the initial reactions in the two age groups, familiarity, and ways to improve the overall Human-Robot Interaction experience potentially.

Robots have the potential to provide everyday life care and support for senior adults, but acceptance is essential for successful implementation in the domestic environment. Nonverbal social behavior can enhance this acceptance, and behavioral cues should be easy and intuitive to understand. However, which factors contribute to senior adults’ intuitive understanding of social cues, such as handshakes? Our research aims to address this question using video observations and semi-structured interviews. Based on a thematic analysis and video observations, our findings indicate that some participants intuitively understood how to shake hands. Most did not shake hands due to not understanding the robot’s behavior or fear. Other identified themes included: contributing features for intuitive handshakes, design improvements, and experiences with the robot’s end effector. Lastly, we found no significant effect between the initial response of the participants to the handshake and either the reaction time or the handshake duration. By designing the gripper and the robot itself in a more familiar, less fear-eliciting way, senior adults might understand the gesture of shaking hands more intuitively.

In social robotics, where people and robots interact in a social context, robot personality design is critical. Through voice, words, gestures, and nonverbal clues, social robots with expressive behaviors can display human-like actions, and the robot’s personality will ensure consistency. This research aims to create robot personalities expressed only by nonverbal cues. Differently from existing studies that test expressive behaviors with non-specialized participants, we look at how and why human movement analysts perceive distinct personalities in robots (introvert vs. extrovert) based on the robot’s movement and other dynamic features, such as joint position, head, and torso position, voice pitch, speed, and so on. We report the findings of a thematic analysis of the data obtained during a focus group with movement analysis experts who watched Pepper robot behaviors designed to be extrovert and introvert. Our findings lead to new guidelines for designing different robot movement features, including body symmetry, personality trait consistency, and social cue congruence during an interaction, all emphasized by the movement analyzers. Finally, we summarize the design principles for extrovert and introvert robot behaviors based on the combined findings of the focus group data analysis and literature review.