Human-robot communication is key to establishing transparency of robot states, and promoting psychological safety of the people interacting with a robot. Such communication is especially challenging for appearance-constrained robots, as they do not afford to employ commonly-used an-thropomorphic modalities such as facial expressions. We explore how an appearance-constrained quadruped robot can use LEDs and gestures to generate affective expressions in order to non-verbally communicate. Traditionally, affect has been used as a signalling paradigm in Human-Robot Interaction where expressions are designed with the goal of communicating a particular emotion such as happiness. Instead, this work explores a generalizable mapping from informative messages (e.g., "I am listening") to abstract affective expressions. We modulate the expressions by a model of affect, taking inspirations from Affect Control Theory (ACT). To explore designing the expressions, we first conducted pilot semi-structured interviews consulting stakeholders who regularly operate appearance-constrained robots (N = 12) to understand the communication requirements. Then, we designed a set of six affective expressions and evaluated them in a crowdsourced study (N = 450). Findings suggest that the expressions can significantly improve effective communication of a robot’s awareness and intent, and promote psychological safety of people interacting with it.

Bullying in schools is a widespread problem with serious consequences. We are exploring the use of social robots and role-playing to foster anti-bullying peer-support. In this paper, we present results from a co-design study with 22 children (8-12 years old) to explore how they envision a “student robot”. To understand how they conceptualize bullying in this context (e.g. whether robots can be bullied), we also investigated how they envision this robot’s various social interactions. We prompted children to imagine a fictional robot about their age and follow a stepwise process to design the robot, and make stories about its interactions. Qualitative analysis of this study suggests themes of robots being described as highly-customizable characters with predominantly positive traits, that are imperfect. We also found that children can articulate various scenarios involving robots taking roles of bullies, victims, and bystanders. These findings contribute insights for designing pedagogical robots and anti-bullying interventions for children.

Robots create a window of opportunity to challenge the barriers that children with special needs face. Robots are physical agents that can be imbued with seemingly "intelligent" behaviours. They can facilitate accessible play by acting as proxies to both children with physical special needs and typically developing children, creating an even playing field. Including target users such as children with special needs in the design process is essential in creating a child-friendly robot that ensures repeated use, engagement and long-term interaction. This paper presents an online approach to involve stakeholders with user-centered design, exemplifying that children can be included in the creation and feedback process even when it is not possible to hold in-person co-design sessions due to COVID-19. We present qualitative findings from a user-centered design study and offer recommendations for designing social robots for accessible play and facilitating child-child interaction.

Bullying in schools is a serious issue with severe and long-term consequences. We explore using social robots in anti-bullying programs to encourage children to intervene in bullying of their peers. To that end, we have conducted a crowdsourced study to explore the feasibility of using robots in the context of bullying (i.e., to investigate whether robots are perceived as entities that can be bullied). We present qualitative and quantitative results from a between-subjects video study, comparing robot bullying (robots being bullied) to human bullying (humans being bullied). Our findings suggest that while the majority of participants describe both instances with connotations of wrongness and immorality, they use different cognitive mechanisms for moral disengagement with robot bullying vs human bullying. We also found significant differences in participants’ perceptions of each scenario, including associating robot mistreatment with bullying less strongly, and being less willing to intervene in it. This work contributes insights for understanding how people perceive bullying of robots, designing intelligent behaviors to discourage bullying of robots, and to our long-term goal of developing anti-bullying pedagogical programs that use social robots.

Artificial intelligence (AI) assistants for clinical decision making show increasing promise in medicine. However, medical assessments can be contentious, leading to expert disagreement. This raises the question of how AI assistants should be designed to handle the classification of ambiguous cases. Our study compared two AI assistants that provide classification labels for medical time series data along with quantitative uncertainty estimates: conventional vs. ambiguity-aware. We simulated our ambiguity-aware AI based on real-world expert discussions to highlight cases likely to lead to expert disagreement, and to present arguments for conflicting classification choices. Our results demonstrate that ambiguity-aware AI can alter expert workflows by significantly increasing the proportion of contentious cases reviewed. We also found that the relevance of AI-provided arguments (selected from guidelines either randomly or by experts) affected experts' accuracy at revising AI-suggested labels. Our work contributes a novel perspective on the design of AI for contentious clinical assessments.

AV-pedestrian interaction will impact pedestrian safety, etiquette, and overall acceptance of AV technology. Evaluating AV-pedestrian interaction is challenging given limited availability of AVs and safety concerns. These challenges are compounded by "mixed traffic" conditions: studying AV-pedestrian interaction will be difficult in traffic consisting of vehicles varying in autonomy level. We propose immersive pedestrian simulators as design tools to study AV-pedestrian interaction, allowing rapid prototyping and evaluation of future AV-pedestrian interfaces. We present OnFoot: a VR-based simulator that immerses participants in mixed traffic conditions and allows examination of their behavior while controlling vehicles' autonomy-level, traffic and street characteristics, behavior of other virtual pedestrians, and integration of novel AV-pedestrian interfaces. We validated OnFoot against prior simulators and Wizard-of-Oz studies, and conducted a user study, manipulating vehicles' autonomy level, interfaces, and pedestrian group behavior. Our findings highlight the potential to use VR simulators as powerful tools for AV-pedestrian interaction design in mixed traffic.

We present Mutation: performing arts based approach that can help decrease the cognitive load associated with cyborg transitioning. Cyborgs are human-machine hybrids with organic and mechatronic body parts that can be implanted or worn. The transition into and out of experiencing additional body parts is not fully understood. Our goal is to draw from performing arts techniques in order to help decrease the cognitive load associated with becoming and unbecoming a cyborg. Actors constantly shift between states, whether from one character to another, or from pre- to post- performance. We contribute a straightforward adaptation of classic performing art practices to cyborg transitioning, and a study where actors used these protocols in order to enter a cyborg state, perform as a cyborg, and then exit the cyborg state. Our work on Mutation suggests that classic performing art practices can be useful in cyborg transitioning, as well as in other technology augmented experiences.

Social robots are being designed to use human-like communication techniques, including body language, social signals, and empathy, to work effectively with people. Just as between people, some robots learn about people and adapt to them. In this paper we present one such robot design: we developed Sam, a robot that learns minimal information about a person's background, and adapts to this background. Our in-the-wild study found that people helped Sam for significantly longer when it adapted to match their background. While initially we saw this as a success, in re-considering our study we started seeing a different angle. Our robot effectively deceived people (changed its story and text), based on some knowledge of their background, to get more work from them. There was little direct benefit to the person from this adaptation, yet the robot stood to gain free labor. We would like to pose the question to the community: is this simply good robot design, or, is our robot being manipulative? Where does the ethical line lay between a robot leveraging social techniques to improve interaction, and the more negative framing of a robot or algorithm taking advantage of people? How can we decide what is good here, and what is less desirable?

We investigated interactive agents using subliminal priming - the act of exposing a person to stimuli that they may not consciously notice, but are still processed subliminally in their mind - in an attempt to shape a person's mood and behavior. We present an overview of the psychology of subliminal priming from the perspective of how it applies to human-agent interaction, including a discussion of the potential ethical and practical implications. We further present the results from two exploratory studies (one in-lab, one crowdsourced) that present potential subliminal-priming interfaces. Our results suggest that subliminal priming may impact how participants perceive an agent and how much they enjoy a task, but we failed to find any effect of priming on participant mood or agent persuasiveness. This work aims to raise awareness of the dangers of subliminal methods of priming and contributes to the discussion on the ethics of social agents.