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We present Project IRL (In Real Life), a suite of five mobile apps we created to explore novel ways of supporting in-person social interactions with augmented reality. In recent years, the tone of public discourse surrounding digital…

Extended Reality (XR) enables immersive experiences through untethered headsets but suffers from stringent battery and resource constraints. Energy-efficient design is crucial to ensure both longevity and high performance in XR devices.…

Inaccurate spatial tracking in extended reality (XR) devices leads to virtual object jitter, misalignment, and user discomfort, fundamentally limiting immersive experiences and natural interactions. In this work, we introduce a novel…

Traditional XR and Metaverse applications prioritize user experience (UX) for adoption and success but often overlook a crucial aspect of user interaction: emotions. This article addresses this gap by presenting an emotion-aware Metaverse…

This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher \textit{interaction} is \textit{limited} during training. Our setting is motivated by the realistic scenarios where a…

Augmented Reality (AR) presents new opportunities for immersive storytelling. However, this immersiveness faces two main hurdles. First, AR's immersive quality is often confined to visual elements, such as pixels on a screen. Second,…

Public speaking anxiety affects many individuals, yet opportunities for real-world practice remain limited. This study explores how augmented reality (AR) can provide an accessible training environment for public speaking. Drawing from…

Federated Continual Learning (FCL) has emerged as a robust solution for collaborative model training in dynamic environments, where data samples are continuously generated and distributed across multiple devices. This survey provides a…

As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they…

Extended Reality (XR) is a rapidly growing field with a wide range of hardware from head mounted displays to installations. Users have the possibility to access the entire Mixed Reality (MR) continuum. Goal of the human-computer-interaction…

Effective personalization on large-scale job platforms requires modeling members based on heterogeneous textual sources, including profiles, professional data, and search activity logs. As recommender systems increasingly adopt Large…

Web-based applications are highly accessible to users, providing rich, interactive content while eliminating the need to install software locally. However, evolutionary robotics (ER) has faced challenges in this domain as web-based…

Understanding a Reinforcement Learning (RL) policy is crucial for ensuring that autonomous agents behave according to human expectations. This goal can be achieved using Explainable Reinforcement Learning (XRL) techniques. Although textual…

Real-time synthesis of physically plausible human interactions remains a critical challenge for immersive VR/AR systems and humanoid robotics. While existing methods demonstrate progress in kinematic motion generation, they often fail to…

Many real-world tasks are intuitive for a human to perform, but difficult to encode algorithmically when utilizing a robot to perform the tasks. In these scenarios, robotic systems can benefit from expert demonstrations to learn how to…

The growing ubiquity of Extended Reality (XR) is driving Conversational Recommendation Systems (CRS) toward visually immersive experiences. We formalize this paradigm as Immersive CRS (ICRS), where recommended items are highlighted directly…

This work presents a new state of the art in reconstruction of surface realizations from obfuscated text. We identify the lack of sufficient training data as the major obstacle to training high-performing models, and solve this issue by…

Reinforcement learning (RL) offers a general approach for modeling and training AI agents, including human-AI interaction scenarios. In this paper, we propose SHARPIE (Shared Human-AI Reinforcement Learning Platform for Interactive…

Firefighters suffer a variety of life-threatening risks, including line-of-duty deaths, injuries, and exposures to hazardous substances. Support for reducing these risks is important. We built a partially occluded object reconstruction…

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning…