Semantic-Free Utterances (SFUs, sounds conveying intention without using words) are being increasingly adopted for human-robot interaction (HRI) to communicate affect. In healthcare, where older adults are overrepresented, affective robotics are becoming more common to reduce healthcare professionals' workload. Hence, understanding how older adults perceive and communicate with robots is crucial. Although previous studies have demonstrated a decline in older adults' ability to categorize emotions, it remains unclear how this impacts their comprehension of SFUs used in HRI. This paper investigates the effect of age (and other factors) on listeners' ability to categorize emotions in SFUs designed for HRI. Additionally, we explore listeners' preferences of SFUs for a healthcare robot. Listeners indicated that SFUs' similarities to natural language, the need for a distinction between human and robot, and their expectations of how a hospital robot should sound like, influenced their preferences. Furthermore, we conducted an online emotion categorization task to investigate how age, emotion category, type of SFU (with varying degrees of robot-likeness), listeners' gender, and their experience with robots relate to listeners' ability to categorize emotions. Results confirm that as age increases, there is a decline in emotion categorization performance of SFUs varying by emotion category and type of SFU.

Semantic-free utterances are thought to lower expectations of robot capabilities.However, to our knowledge, it still needs to be investigated how the type of robot communication (semantic-free utterance vs natural language), appearance (humanlike vs robotlike), and perceived voice-appearance alignment influence perceived robot capabilities.In a 2x2 online study, participants viewed videos with robots varying in communication and appearance.Linear mixed modelling showed that higher perceived voice-appearance alignment ratings were associated with higher perceived ratings of robot competency and agency.Contrary to expectations, semantic-free utterances and robot-like appearance were not significantly associated with perceived competency and agency.

To enhance the design of gender-ambiguous speaking robots, designers could benefit from more insights into how people integrate robot appearance and voice in the gender perception of robots. In an online survey, we presented audio-only, visual-only, and audiovisual displays of gendered (and gender-ambiguous) robots and asked participants to rate the perceived level of femininity, masculinity, and gender-ambiguity of these robots. We investigated how the addition of an embodiment feature (voice, robot appearance) or gender (feminine, masculine, ambiguous) affected the perceived gender of speaking robots. Results showed a complex interplay between the variables under study: the magnitude and direction of effect on perceived gender are dependent on the embodiment feature, the gender of the added feature, and the gender identity of the basis that was added to. Furthermore, we found that the addition of a voice to a gender-ambiguous identity has more impact than the addition of a robot appearance on the perceived gender-ambiguity by lowering perceived ambiguity. For the design of gender-ambiguous robots, efforts should focus on making the appearance more ambiguous to counterbalance the gendered effect of voice.

With the deployment of robots in public realms, researchers are seeing more and more cases of abusive disinhibition towards robots. Because robots embody gendered identities, poor navigation of antisocial dynamics may reinforce or exacerbate gender-based violence. Robots deployed in social settings must recognize and respond to abuse in a way that minimizes ethical risk. This will require designers to first understand the risk posed by abuse of robots, and how humans perceive robot-directed abuse. To that end, we conducted an exploratory study of reactions to a physically abusive interaction between a human perpetrator and a victimized agent. Given extensions of gendered biases to robotic agents, as well as associations between an agent’s human likeness and the experiential capacity attributed to it, we quasi-manipulated the victim’s humanness (via use of a human actor vs. NAO robot) and gendering (via inclusion of stereotypically masculine vs. feminine cues in their presentation) across four video-recorded reproductions of the interaction. Analysis of data from 417 participants, each of whom watched one of the four videos, indicates that the intensity of emotional distress felt by an observer is associated with their gender identification, previous experience with victimization, hostile sexism, and support for social stratification, as well as the victim’s gendering.

Inspired by prior work with robots that physically display positive emotion (e.g., [1]), we were interested to see how people might interact with a robot capable of communicating cues of negative affect such as anger. Based in particular on [2], we have prototyped an anti-social, zoomorphic robot equipped with a spike mechanism to nonverbally communicate anger. The robot's embodiment involves a simple dome-like morphology with a ring of inflatable spikes wrapped around its circumference. Ultrasonic sensors engage the robot's antisocial cuing (e.g., “spiking” when a person comes too close). To evaluate people's perceptions of the robot and the impact of the spike mechanism on their behavior, we plan to deploy the robot in social settings where it would be inappropriate for a person to approach (e.g., in front of a door with a “do not disturb” sign). We expect that exploration of robot antisociality, in addition to prosociality, will help inform the design of more socially complex human-robot interactions.