3.1 Environment Recognition
In the execution of a Computer Control Agent (CCA), Environment Perception is one of its core capabilities, directly determining the agent’s understanding and adaptability to the computer operating environment. To achieve efficient and precise environment perception, we adopt a Multi-Modal Representation approach, gathering information from different Observation Spaces and leveraging Fusion Techniques to enhance the agent’s comprehension of its surroundings.
Image Screen Representation: Captures the computing environment through screenshots, making it applicable to various platforms such as web, Android, and PC, closely resembling human visual perception. Its strength lies in scalability, but it comes with higher processing complexity. Optimization focuses on identifying key visual elements and simplifying image data.
Textual Screen Representation: Extracts structured text data to analyze UI and environment information. It offers structured, high-precision parsing but has lower scalability. Text optimization includes key element extraction and summarization.
Multi-Modal Representation: Combines image and text inputs for environmental perception, with potential future expansion to video and audio modalities. This approach integrates the strengths of both image and text while enhancing scalability to adapt to a broader range of operational environments.
However, multi-modal perception introduces additional technical challenges:
Information Overload: Multiple data sources may introduce irrelevant information that disrupts decision-making.
Modality Alignment: Integrating text and image data remains an open research problem.
Computational Cost: Multi-modal models have high inference costs, requiring optimization strategies.
These are key challenges we are actively researching and addressing.
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