Augmented reality task guidance systems provide assistance for procedural tasks, which require a sequence of physical actions, by rendering virtual guidance visuals within the real-world environment. An example of such a task would be to secure two wood parts together, which could display guidance visuals indicating the user to pick up a drill and drill each screw. Current AR task guidance systems are limited in that they require AR system experts for use, require CAD models of real-world objects, or only function for limited types of tasks or environments. We propose a general-purpose AR task guidance approach and proof-of-concept system to generate guidance for tasks defined by natural language. Our approach allows an operator to take pictures of relevant objects and write task instructions for an end user, which are used by the system to determine where to place guidance visuals. Then, an end user can receive and follow guidance even if objects change location or environment. Guidance includes reusable visuals that display generic actions, such as our system's 3D hand animations. Our approach utilizes current vision-language machine learning models for text and image semantic understanding and object localization. We built a proof-of-concept system using our approach and tested its accuracy and usability in a user study. We found that all operators were able to generate clear guidance for tasks in an office room, and end users were able to follow the guidance visuals to complete the expected action 85.7% of the time without knowledge of their tasks. Participants rated that our system was easy to use to generate guidance visuals they expected. / Master of Science / Augmented Reality (AR) task guidance systems provide assistance for tasks by placing virtual guidance visuals on top of the real world through displays. An example of such a task would be to secure two wood parts together, which could display guidance visuals indicating the user to pick up a drill and drill each screw. Current AR task guidance systems are limited in that they require AR system experts for use, require detailed models of real-world objects, or only function for limited types of tasks or environments. We propose a new task guidance approach and built a system to generate guidance for tasks defined by written instructions. Our approach allows an operator to take pictures of relevant objects and write task instructions for an end user, which are used by the system to determine where to place digital visuals. Then, an end user can receive and follow guidance even if objects change location or environment. Guidance includes visuals that display generic actions, such as our system's 3D hand animations that mimic human hand actions. Our approach utilizes AI models for text and image understanding and object detection. We built a proof-of-concept system using our approach and tested its accuracy and usability in a user study. We found that all operators were able to generate clear guidance for tasks in an office room, and end users were able to follow the guidance visuals to complete the expected action 85.7% of the time without knowledge of the tasks. Participants rated that our system made it easy to write instructions and take pictures to create guidance visuals.

Over the last few years, there have been striking developments in wearable computing. Among all the different forms of wearable devices, Head Mounted Displays (HMDs) are deemed the first seamless solution to enabling workers with real time contextual information and allowing companies to integrate with existing back-end systems. The hands-free feature that come along with the HMDs is also believed a great advantage over many traditional technologies. However, few studies had discussed the impact of different design characteristics of head mounted displays on task performance. This study aimed to find out how different display positions of Head Mounted Displays may affect the performance of workers performing guided repair and maintenance tasks. A set of car maintenance tasks were performed by 20 participants with task guidance presented at four Display Conditions: above-eye HMD, eye-centered HMD, below-eye HMD and the traditional paper manual. Time and errors were measured and discussed, as well as other user experience related measurements.