We have developed a Communal eXtended-Reality (CXR) system that enables groups of people in a shared moving vehicle to view a common geo-synchronized tour. This paper describes the geo-synchronized multi-user extended reality system we created to provide a situated and shared experience to promote community engagement. This paper describes: (a) the technical implementation of the CXR system, which geo-locates and orients the view of the participant within the moving vehicle; (b) the immersive digital twin tour, critically aligned with the real-life location; (c) our fallback system, which allows people who feel disoriented or motion-sick to continue along with the content of the tour. We validated the sense of communality, comfort, and effectiveness of the system through in-ride observation and post-ride surveys. Our intent is to enable development of similar systems to foster communal engagement in communities worldwide.

While great strides have been taken in advancing the field of Human-Robot Interaction (HRI), challenges abound in understanding and improving how Autonomous Vehicles (AVs) will interact with and within society. Through this paper, the authors attempt to paint the picture of challenges unique to the study and advancement of interfaces between AVs and vulnerable road users (VRUs). In turn, these gaps in research highlight the opportunities for academia, industry, and public policy to collaborate and advance the state of the art of AV-VRU interaction, and the need for a dedicated forum for sharing insights across these various sectors.

Robots may be able to significantly assist older adults through making activity recommendations. Prior research suggests that gender and age of a robot’s voice may affect how people respond to such recommendations, but few studies have explored how a robot’s voice is perceived by older adults, and whether their perceptions differ across cultures. We conducted a survey study with older adult participants (aged 65+) in the U.S. (N=225) and Japan (N=466), asking them to evaluate a humanoid robot speaking with three different voices (male, female, child). After seeing a video of a robot making recommendations, participants rated the fit of the voice to the robot, its sociality (via the Robotic Social Attributes Scale - RoSAS), and their willingness to use the robot in various contexts. We discovered that robot’s social attributes and participants’ culture impacted willingness to use the robot in both countries. Having positive social attributes and lower negative attributes increases willingness to use the robot. The U.S. older adults preferred the adult robot voices, had more positive social attributes, less negative social attributes, and were more likely to accept lifestyle recommendations than Japanese older adults. This study contributes to our understanding of older adults’ perceptions of robot voice and provides design implications for robots that make recommendations to older adults.

The growing robot adoption in real-world communities emphasizes the role of community factors in promoting robot acceptance and interaction. This paper advocates for formalizing community involvement to expand industry-academia collaboration in HRI. It explores the importance of community in HRI, highlights unique aspects of academia-industry relationships resulting from community engagement, by sharing examples and a community involvement experience report. The paper also proposes a framework and considerations for sustainable collaboration with the community, aiming to unlock the full potential of HRI research. Lastly, it envisions generative communal labs as a way to tackle unresolved challenges of long-term integration of robots into communities and achievement of positive social and community impact.

Geriatric depression is a common mental health condition affecting majority of older adults in the US. As per Attention Restoration Theory (ART), participation in outdoor activities is known to reduce depression and provide restorative benefits. However, many older adults, who suffer from depression, especially those who receive care in organizational settings, have less access to sensory experiences of the outdoor natural environment. This is often due to their physical or cognitive limitations and from lack of organizational resources to support outdoor activities. To address this, we plan to study how technology can bring the restorative benefits of outdoors to the indoor environments through augmented spatial natural soundscapes. Thus, we propose an interview and observation-based study at an assisted living facility to evaluate how augmented soundscapes substitute for outdoor restorative, social, and experiential benefits. We aim to integrate these findings into a minimally intrusive and intuitive design of an interactive augmented soundscape, for indoor organizational care settings.

Social robots have been designed to engage with older adults and children separately, but their use for intergenerational (IG) interactions, especially in nonfamilial settings, has not been studied. In addition to the challenge of simultaneously meeting the varied needs and preferences of older adults and children, the dynamic nature of these settings makes the use of robots for IG activities difficult. This paper presents a first exploratory study meant to inform the design and use of social robots for IG activities in nonfamilial settings by analyzing interviews and observations conducted at a co-located preschool and assisted living-dementia care center. Interactions occurring with and around robots were analyzed, particularly focusing on whether they fulfill the community's goals of providing children and older adults with engaging opportunities for IG contact. Findings suggest integrating intermittent pauses and breaks in interactions with the robot and unstructured collaborative robot-assisted activities can meet the needs of both generations, and call for greater community involvement in HRI for IG research.

Recent years have seen robust advancements in robotic platforms for multiple users, while HRI research is increasingly examining small group interactions. However, there has been little consideration of appropriate methodologies for design or development of human-robot interactions to foster and enhance context-specific shared goals, interactions, and experiences within larger communities. This paper presents a preliminary study using a community-centered approach to collective perceptions about shared social robots in a retirement village. It reveals novel aspects regarding people's sense of community, community roles and purposes of robots. Findings indicate need of a framework for community robotics and further studies using a community perspective to bring rich insight into goal oriented and context specific multi-user experiences and interactions in HRI.

A common challenge when dealing with heterogenous tasks such as face expression analysis, face and object recognition is high dimensionality and extreme appearance variations within each class. To handle such scenarios, we formulate a supervised Non-negative Matrix Factorization (NMF) based subspace learning technique that simultaneously preserves the intra-class regression information (local) and enhances inter-class discrimination (global) in the low dimensional embedding. Our method leverages the multi-dimensional image labels that quantify the within class regression to learn the subspaces for recognition. In addition, our formulation includes a novel multi-output regression based NMF algorithm.

Our paper presents a novel high dimensional probability density estimation technique using any dimensionality reduction method. Our method first performs subspace reduction using any matrix factorization algorithm and estimates the density in the low-dimensional space using sample-point variable bandwidth kernel density estimation. Subsequently, the high dimensional density is approximated from the low dimensional density parameters. The reconstruction error due to dimensionality reduction process is also modeled in a principled and efficient manner to obtain the high dimensional density estimate. We show the effectiveness of our technique by using two popular dimensionality reduction tools, principal component analysis and non-negative matrix factorization. This technique is applied to AT&T, Yale, Pointing'04 and CMU-PIE face recognition datasets and improved performance compared to other dimensionality reduction and density estimation algorithms is obtained.

Non-negative matrix factorization (NMF) is an excellent tool for unsupervised parts-based learning, but proves to be ineffective when parts of a whole follow a specific pattern. Analyzing such local changes is particularly important when studying anatomical transformations. We propose a supervised method that incorporates a regression constraint into the NMF framework and learns maximally changing parts in the basis images, called Regression based NMF (RNMF). The algorithm is made robust against outliers by learning the distribution of the input manifold space, where the data resides. One of our main goals is to achieve good region localization. By incorporating a gradient smoothing and independence constraint into the factorized bases, contiguous local regions are captured. We apply our technique to a synthetic dataset and structural MRI brain images of subjects with varying ages. RNMF finds the localized regions which are expected to be highly changing over age to be manifested in its significant basis and it also achieves the best performance compared to other statistical regression and dimensionality reduction techniques.

Non-negative matrix factorization (NMF) has proven to be useful in image classification applications such as face recognition. We propose a novel discriminative basis selection method for classification of image categories based on the popular term frequency-inverse document frequency (TF-IDF) weight used in information retrieval. We extend the algorithm to incorporate color, and overcome the drawbacks of using unaligned images. Our method is able to choose visually significant bases which best discriminate between categories and thus prune the classification space to increase correct classifications. We apply our technique to ETH-80, a standard image classification benchmark dataset. Our results show that our algorithm outperforms other state-of-the-art techniques.