This work introduces ArtAdapter, a transformative text-to-image (T2I) style transfer framework that transcends tra-ditional limitations of color, brushstrokes, and object shape, capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with our proposed explicit adaptation mechanism enables ArtAdapter to achieve unprecedented fidelity in style transfer, ensuring close alignment with tex-tual descriptions. Additionally, the incorporation of an Aux-iliary Content Adapter (ACA) effectively separates content from style, alleviating the borrowing of content from style references. Moreover, our novel fast finetuning approach could further enhance zero-shot style representation while mitigating the risk of overfitting. Comprehensive evaluations confirm that ArtAdapter surpasses current state-of-the-art methods.

A growing number of studies use a "ghost-driver" vehicle driven by a person in a car seat costume to simulate an autonomous vehicle. Using a hidden-driver vehicle in a field study in the Netherlands, Study 1 (N = 130) confirmed that the ghostdriver methodology is valid in Europe and confirmed that European pedestrians change their behavior when encountering a hidden-driver vehicle. As an important extension to past research, we find pedestrian group size is associated with their behavior: groups look longer than singletons when encountering an autonomous vehicle, but look for less time than singletons when encountering a normal vehicle. Study 2 (N = 101) adapted and extended the hidden-driver method to test whether it is believable as online video stimuli and whether car characteristics and participant feelings are related to the beliefs and behavior of pedestrians who see hidden-driver vehicles. As expected, belief rates were lower for hidden-driver vehicles seen in videos compared to in a field study. Importantly, we found noticing no driver was the only significant predictor of belief in car autonomy, which reinforces prior justification for the use of the ghostdriver method. Our contributions are a replication of the hidden-driver method in Europe and comparisons with past US and Mexico data; an extension and evaluation of the ghostdriver method in video form; evidence of the necessity of the hidden driver in creating the illusion of vehicle autonomy; and an extended analysis of how pedestrian group size and feelings relate to pedestrian behavior when encountering a hidden-driver vehicle.

Many of the problems we face are solved in small groups. Using decades of research from psychology, HRI research is increasingly trying to understand how robots impact the dynamics and outcomes of these small groups. Current work almost exclusively uses humanoid robots that take on the role of an active group participant to influence interpersonal dynamics. We argue that this has limitations and propose an alternative design approach of using a peripheral robotic object. This paper presents Micbot, a peripheral robotic object designed to promote participant engagement and ultimately performance using nonverbal implicit interactions. The robot is evaluated in a 3 condition (no movement, engagement behaviour, random movement) laboratory experiment with 36 three-person groups (N=108). Results showed that the robot was effective in promoting not only increased group engagement but also improved problem solving performance. In the engagement condition, participants displayed more even backchanneling toward one another, compared to no movement, but not to the random movement. This more even distribution of backchanneling predicted better problem solving performance.

Interpersonal distance behaviors can vary significantly across countries and impact human social interaction. Do these cross-cultural differences play out when one of the interaction partners participates through a teleoperated robot? Emerging research shows that when being approached by a robot, people tend to hold similar cultural preferences as they would for an approaching human. However, no work yet has investigated this question from a robot teleoperator's perspective. Toward answering this, we conducted an online study (N = 774) using a novel simulation paradigm across two countries (U.S. and India). Results show that in the role of a telepresence robot operator, participants exhibited cross-cultural differences in interpersonal distance behavior in line with human-human proxemic research, indicating that culture-specific distance behavior can manifest in the way a robot operator controls a robot. We discuss implications for designers who seek to automate path planning and navigation for teleoperated robots.

How do sounds shape interaction with robots? The present study explores aural impressions associated with servo motors commonly used to prototype robotic motion. This exploratory analysis constructs a framework to objectively and subjectively characterize sound using acoustic analyses and novice evaluators on Amazon Mechanical Turk. Participants evaluated unfamiliar sounds through pairwise comparison, resulting in subjective ratings of servo motor sounds. In this study, subjective measures of sound correlated well internally, but correlated weakly with objective measures. Moreover, qualitative commentary offered by participants suggests both anthropomorphic associations with sounds as well as negative impressions of the sounds overall. We conclude with a roadmap for exploration into the field of consequential sonic interaction design.

How does a robot's sound shape our perception of it? We overlaid sound from high-end and low-end robot arms on videos of the high-end KUKA youBot desktop robotic arm moving a small block in functional (working in isolation) and social (interacting with a human) contexts. The low-end audio was sourced from an inexpensive OWI arm. Crowdsourced participants watched one video each and rated the robot along dimensions of competence, trust, aesthetic, and human-likeness. We found that the presence and quality of sound shapes subjective perception of the KUKA arm. The presence of any sound reduced human-likeness and aesthetic ratings, however the high-end sound rated better in the competence evaluation in the social context measures when compared to no sound. Overall, the social context increased the perceived competence, trust, aesthetic and human-likeness of the robot. Based on motor sound's significant mixed impact on visual perception of robots, we discuss implications for sound design of interactive systems.

This tutorial is a hands-on introduction to human-centered design topics and practices for human-robot interaction. It is intended for researchers with a variety of backgrounds, particularly those with little or no prior experience in design. In the morning, participants will learn about user needs and needfinding, as ways to understand the stakeholders in research outcomes, guide the selection of participants, and as possible measures of success. We then focus on design sketching, including ways to represent objects, people and their interactions through storyboards. Design sketching is not intended to be art, rather a way to develop and build upon ideas with oneself, and quickly communicate with colleagues. In the afternoon, participants will use the tools and materials, and learn techniques for lightweight physical prototyping and improvisation. Participants will build a small paper robot (not actuated) of their own design, to practice puppeteering, explore bodily movement and prototype interactions.