EVA Introduction Material
· 2 min read
This document provides a comprehensive overview of the detailed configuration and technical vision of EVA.
[Overview]
EVA serves as 'The Brain of Physical AI,' powered by a Multi-Foundation Model that integrates Vision Models (VM), Vision-Language Models (VLM) and Large Language Models (LLM). It goes beyond simple object detection; by leveraging VLM, EVA understands complex visual contexts and situational nuances, acting as an intelligent agent that makes autonomous decisions aligned with user intent.
[Key Highlights]
- Multi-Foundation Model Architecture: Foundation models for vision (VM), vision-language (VLM), and large language (LLM) are organically linked to analyze scenes from multiple perspectives and make common-sense judgments.
- Interactive Scenario Setting: Define detection scenarios using natural language without complex coding. Users can refine AI performance in real-time through conversational feedback.
- Human-in-the-Loop: User feedback is immediately incorporated into the learning process, allowing the vision agent to become increasingly optimized for specific environments over time.
- Closing the Loop (Action): Beyond situational awareness, EVA completes the loop by executing physical actions, such as robot control and facility management, to resolve issues.
[Resources]
For more details, please refer to the document below.
