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The concept of augmented reality (AR) assistants has captured the humanimagination for decades, becoming a staple of modern science fiction. To pursuethis goal, it is necessary to develop artificial intelligence (AI)-basedmethods that simultaneously perceive the 3D environment, reason about physicaltasks, and model the performer, all in real-time. Within this framework, a widevariety of sensors are needed to generate data across different modalities,such as audio, video, depth, speech, and time-of-flight. The required sensorsare typically part of the AR headset, providing performer sensing andinteraction through visual, audio, and haptic feedback. AI assistants not onlyrecord the performer as they perform activities, but also require machinelearning (ML) models to understand and assist the performer as they interactwith the physical world. Therefore, developing such assistants is a challengingtask. We propose ARGUS, a visual analytics system to support the development ofintelligent AR assistants. Our system was designed as part of a multi year-longcollaboration between visualization researchers and ML and AR experts. Thisco-design process has led to advances in the visualization of ML in AR. Oursystem allows for online visualization of object, action, and step detection aswell as offline analysis of previously recorded AR sessions. It visualizes notonly the multimodal sensor data streams but also the output of the ML models.This allows developers to gain insights into the performer activities as wellas the ML models, helping them troubleshoot, improve, and fine tune thecomponents of the AR assistant.

ARGUS: Visualization of AI-Assisted Task Guidance in AR