Robotic guidance systems have shown promise in supporting blind and visually impaired (BVI) individuals with wayfinding and obstacle avoidance. However, most existing systems assume a clear path and do not support a critical aspect of navigation—environmental interactions that require manipulating objects to enable movement. These interactions are challenging for a human–robot pair because they demand (i) precise localization and manipulation of interaction targets (e.g., pressing elevator buttons) and (ii) dynamic coordination between the user’s and robot’s movements (e.g., pulling out a chair to sit). We present a collaborative human–robot approach that combines our robotic guide dog’s precise sensing and localization capabilities with the user’s ability to perform physical manipulation. The system alternates between two modes: lead mode, where the robot detects and guides the user to the target, and adaptation mode, where the robot adjusts its motion as the user interacts with the environment (e.g., opening a door). Evaluation results show that our system enables navigation that is safer, smoother, and more efficient than both a traditional white cane and a non-adaptive guiding system, with the performance gap widening as tasks demand higher precision in locating interaction targets. These findings highlight the promise of human–robot collaboration in advancing assistive technologies toward more generalizable and realistic navigation support.

Unlike the free exploration of childhood, the demands of daily life reduce our motivation to explore our surroundings, leading to missed opportunities for informal learning. Traditional tools for knowledge acquisition are reactive, relying on user initiative and limiting their ability to uncover hidden interests. Through formative studies, we introduce AiGet, a proactive AI assistant integrated with AR smart glasses, designed to seamlessly embed informal learning into low-demand daily activities (e.g., casual walking and shopping). AiGet analyzes real-time user gaze patterns, environmental context, and user profiles, leveraging large language models to deliver personalized, context-aware knowledge with low disruption to primary tasks. In-lab evaluations and real-world testing, including continued use over multiple days, demonstrate AiGet's effectiveness in uncovering overlooked yet surprising interests, enhancing primary task enjoyment, reviving curiosity, and deepening connections with the environment. We further propose design guidelines for AI-assisted informal learning, focused on transforming everyday moments into enriching learning experiences. © 2025 Copyright held by the owner/author(s).

Imagine a future when we can Zoom-call a robot to manage household chores remotely. This work takes one step in this direction. Robi Butler is a new household robot assistant that enables seamless multimodal remote interaction. It allows the human user to monitor its environment from a first-person view, issue voice or text commands, and specify target objects through hand-pointing gestures. At its core, a high-level behavior module, powered by Large Language Models (LLMs), interprets multimodal instructions to generate multistep action plans. Each plan consists of open-vocabulary primitives supported by vision-language models, enabling the robot to process both textual and gestural inputs. Zoom provides a convenient interface to implement remote interactions between the human and the robot. The integration of these components allows Robi Butler to ground remote multimodal instructions in real-world home environments in a zero-shot manner. We evaluated the system on various household tasks, demonstrating its ability to execute complex user commands with multimodal inputs. We also conducted a user study to examine how multimodal interaction influences user experiences in remote human-robot interaction. These results suggest that with the advances in robot foundation models, we are moving closer to the reality of remote household robot assistants.

While effective for recording and sharing experiences, traditional in-context writing tools are relatively passive and unintelligent, serving more like instruments rather than companions. This reduces primary task (e.g., travel) enjoyment and hinders high-quality writing. Through formative study and iterative development, we introduce PANDALens, a Proactive AI Narrative Documentation Assistant built on an Optical See-Through Head Mounted Display that supports personalized documentation in everyday activities. PANDALens observes multimodal contextual information from user behaviors and environment to confirm interests and elicit contemplation, and employs Large Language Models to transform such multimodal information into coherent narratives with significantly reduced user effort. A real-world travel scenario comparing PANDALens with a smartphone alternative confirmed its effectiveness in improving writing quality and travel enjoyment while minimizing user effort. Accordingly, we propose design guidelines for AI-assisted in-context writing, highlighting the potential of transforming them from tools to intelligent companions.

Interactions with digital devices during social settings can reduce social engagement and interrupt conversations. To overcome these drawbacks, we designed ParaGlassMenu, a semi-transparent circular menu that can be displayed around a conversation partner’s face on Optical See-Through Head-Mounted Display (OHMD) and interacted subtly using a ring mouse. We evaluated ParaGlassMenu with several alternative approaches (Smartphone, Voice assistant, and Linear OHMD menus) by manipulating Internet-of-Things (IoT) devices in a simulated conversation setting with a digital partner. Results indicated that the ParaGlassMenu offered the best overall performance in balancing social engagement and digital interaction needs in conversations. To validate these findings, we conducted a second study in a realistic conversation scenario involving commodity IoT devices. Results confirmed the utility and social acceptance of the ParaGlassMenu. Based on the results, we discuss implications for designing attention-maintaining subtle interaction techniques on OHMDs.

Optical see-through head-mounted displays (OHMDs) can provide just-in-time digital assistance to users while they are engaged in ongoing tasks. However, given users’ limited attentional resources when multitasking, there is a need to concisely and accurately present information in OHMDs. Existing approaches for digital information presentation involve using either text or pictograms. While pictograms have enabled rapid recognition and easier use in warning messages and traffic signs, most studies using pictograms for digital notifications have exhibited unfavorable results. We thus conducted a series of four iterative studies to understand how we can support effective notification presentation on OHMDs during multitasking scenarios. We find that while icon-augmented notifications can outperform text-only notifications, their effectiveness depends on icon familiarity, encoding density, and environmental brightness. We reveal design implications when using icon-augmented notifications in OHMDs and present plausible reasons for the observed disparity in literature.

Optical see-through Head-Mounted Displays (OST HMDs, OHMDs) are known to facilitate situational awareness while accessing secondary information. However, information displayed on OHMDs can cause attention shifts, which distract users from natural social interactions. We hypothesize that information displayed in paracentral and near-peripheral vision can be better perceived while the user is maintaining eye contact during face-to-face conversations. Leveraging this idea, we designed a circular progress bar to provide progress updates in paracentral and near-peripheral vision. We compared it with textual and linear progress bars under two conversation settings: a simulated one with a digital conversation partner and a realistic one with a real partner. Results show that a circular progress bar can effectively reduce notification distractions without losing eye contact and is more preferred by users. Our findings highlight the potential of utilizing the paracentral and near-peripheral vision for secondary information presentation on OHMDs.

On-the-go text-editing is difficult, yet frequently done in everyday lives. Using smartphones for editing text forces users into a heads-down posture which can be undesirable and unsafe. We present EYEditor, a heads-up smartglass-based solution that displays the text on a see-through peripheral display and allows text-editing with voice and manual input. The choices of output modality (visual and/or audio) and content presentation were made after a controlled experiment, which showed that sentence-by-sentence visual-only presentation is best for optimizing users' editing and path-navigation capabilities. A second experiment formally evaluated EYEditor against the standard smartphone-based solution for tasks with varied editing complexities and navigation difficulties. The results showed that EYEditor outperformed smartphones as either the path OR the task became more difficult. Yet, the advantage of EYEditor became less salient when both the editing and navigation was difficult. We discuss trade-offs and insights gained for future heads-up text-editing solutions.