Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality

Abstract

Augmented reality (AR) has become a powerful tool for assisting operators in complex environments, such as shop floors, laboratories, and industrial settings. By displaying synthetic visual elements anchored in real environments and providing information for specific tasks, AR helps to improve efficiency and accuracy. However, a common bottleneck in these environments is introducing all necessary information, which often requires predefined structured formats and needs more ability for multimodal and Natural Language (NL) interaction. This work proposes a new method for dynamically documenting complex environments using AR in a multimodal, non-structured, and interactive manner. Our method employs Large Language Models (LLMs) to allow experts to describe elements from the real environment in NL and select corresponding AR elements in a dynamic and iterative process. This enables a more natural and flexible way of introducing information, allowing experts to describe the environment in their own words rather than being constrained by a predetermined structure. Any operator can then ask about any aspect of the environment in NL to receive a response and visual guidance from the AR system, thus allowing for a more natural and flexible way of introducing and retrieving information. These capabilities ultimately improve the effectiveness and efficiency of tasks in complex environments.