Achieving robotic touch that is accepted and trusted by human users requires a clear understanding of touch parameters and how they influence perceptions of safety and comfort. We explore such parameters through a participatory experiment and generate design guidelines for robotic touch, involving 20 participants in designing, evaluating, and iterating robotic parameters for instrumental touch using a robotic arm equipped with a robotic hand. Through our user study, we find that parameters such as robot’s speed, force, and motion, contact surface material, and the robot hand pose impact users’ perception of safety, comfort, intuitiveness, efficiency, effectiveness, trust, and sense of agency. We also find that the choice of robotic parameters is task-dependent and body-part dependent, and that these parameters inter-correlate with each other. Based on these findings, we provide design guidelines for generating user-friendly robotic touch interactions in instrumental touch contexts.

Given large language models’ (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language suggested by LLMs may cause feelings of alienation and result in increased labor for individuals or groups whose style does not match. We examine how such writer-style bias impacts inclusion, control, and ownership over the work when co-writing with LLMs. In an online experiment, participants wrote hypothetical job promotion requests using either hesitant or self-assured auto-complete suggestions from an LLM and reported their subsequent perceptions. We found that the style of the AI model did not impact perceived inclusion. However, individuals with higher perceived inclusion did perceive greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Feelings of inclusion mitigated a loss of control and agency when accepting more AI suggestions.

In this paper, we set out to explore how people judge the personality of a non-anthropomorphic virtual agent during group interactions. Using a Wizard of Oz (WoZ) based approach, we observed that people judged the acceptability of a virtual agent’s behavior from a tool-based lens, that is, if this robot and its behavior are useful to the team or not. Furthermore, we found that while people were able to acknowledge the virtual agent’s personality and recognize its identity through social cues, the tool-based framing impacts these perceptions into a normative judgment of the robots’ utility. We present two case studies that we think highlight this tool-based interpretation of robotic personality: robots expressing non-factual opinions and robots expressing humor. Finally, we suggest that researchers should consider the impact of this tool-based framing on people’s perceptions of a robot’s identity when designing robots for social interaction.

Augmented Reality (AR) glasses separate dyadic interactions on different sides of the lens, where the person wearing the glasses (primary user) sees an AR world overlaid on their partner (secondary actor). The secondary actor interacts with the primary user understanding they are seeing both physical and virtual worlds. We use grounded theory to study interaction tasks, participatory design sessions, and in-depth interviews of 10 participants and explore how AR real-time modifications affect them. We observe a power imbalance attributed to the: (1) lack of transparency of the primary user’s view, (2) violation of agency over self-presentation, and (3) discreet recording capabilities of AR glasses. This information asymmetry leads to a negotiation of behaviors to reach a silently understood equilibrium. This paper addresses underlying design issues that contribute to power imbalances in dyadic interactions and offers nuanced insights into the dynamics between primary users and secondary actors.

This paper introduces CORAE, a novel web-based open-source tool for COntinuous Retrospective Affect Evaluation, designed to capture continuous affect data about interpersonal perceptions in dyadic interactions. Grounded in behavioral ecology perspectives of emotion, this approach replaces valence as the relevant rating dimension with approach and withdrawal, reflecting the degree to which behavior is perceived as increasing or decreasing social distance. We conducted a study to experimentally validate the efficacy of our platform with 24 participants. The tool’s effectiveness was tested in the context of dyadic negotiation, revealing insights about how interpersonal dynamics evolve over time. We find that the continuous affect rating method is consistent with individuals’ perception of the overall interaction. This paper contributes to the growing body of research on affective computing and offers a valuable tool for researchers interested in investigating the temporal dynamics of affect and emotion in social interactions.