AI-assisted remote guidance using augmented reality
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Solution Overview
Problem
Existing augmented reality systems for providing guidance on physical objects are resource-intensive and inefficient, especially on resource-constrained devices, as they require real-time detection and animation of object components, leading to high power consumption and reduced user experience.
Innovation Solution
A remote guidance system using a large language model to generate augmented reality experiences by processing multi-modal inputs, identifying physical objects, and generating digital representations of actions as gesture-icons overlaid on a digital twin of the object, reducing the need for real-time component detection and animation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time detection and animation of object components is performed, then guidance accuracy is improved, but power consumption increases and device resources are overwhelmed
Solution Approach 1:
The system pre-generates a digital twin model of the physical object before the user session begins. This digital twin contains all necessary component information, spatial relationships, and animation data pre-computed and stored, eliminating the need for real-time detection and processing during the guidance session, thus reducing power consumption while maintaining guidance accuracy.
2Measurement precision
If real-time detection and animation of object components is performed, then guidance accuracy is improved, but device complexity increases
Solution Approach 1:
The system creates a digital copy (digital twin) of the physical object that replicates its structure, components, and behavior. This digital twin serves as a simplified representation that can be manipulated and animated without requiring the complex real-time processing of the actual physical object, thereby reducing device complexity while maintaining guidance accuracy.
3Reliability
If comprehensive object analysis is performed, then guidance quality is improved, but processing time increases
Solution Approach 1:
The system performs comprehensive object analysis and generates the digital twin model in advance, before the user needs guidance. All processing, including component identification, spatial relationship mapping, and animation sequence preparation, is completed during this pre-processing phase. During the actual guidance session, the system only needs to retrieve and display pre-computed information, dramatically reducing processing time while maintaining high guidance quality.
Data Source
AI summary
Technology embodied in a computer-implemented method for receiving a multi-modal input representing a query associated with a physical object, processing the multi-modal input to identify the physical object, and determining, based in part on an identification of the physical object and by accessing a language processing model, at least one response to the query associated with the physical object. The method also includes determining a sequence of actions associated with the at least one response, the sequence including at least one action that involves an interaction with at least one portion of the physical object. The method further includes generating a digital representation of the at least one action, and providing the digital representation to a user-device for presentation on a display. The digital representation includes a gesture-icon representing the action, the gesture-icon being overlaid on a digital twin of the physical object.