4.Multimodal visual understand speech interaction

1. Concept Introduction

1.1 What is "Visual Understanding"?

In the largemodel project, the multimodal visual understanding feature enables robots to go beyond simply "seeing" a matrix of pixels and truly "understand" the content, objects, scenes, and relationships within an image. This is like giving robots a pair of thinking eyes.

The core tool for this feature is **seewhat`. When a user issues a command such as "see what's here," the system invokes this tool, triggering a series of background operations that ultimately provide the user with AI-generated analysis of the live image in natural language.

1.2 Brief Implementation Principle

The basic principle is to feed two different types of information—image (visual information) and text (linguistic information)—into a powerful multimodal large model (such as LLaVA).

1. Image Encoding: The model first uses a visual encoder to convert the input image into a computer-interpretable numerical vector. This vector captures image features such as color, shape, and texture.
2. Text Encoding: Simultaneously, the user's question (e.g., "What's on the table?") is also converted into a text vector.
3. Cross-modal Fusion: The most critical step is to fuse the image and text vectors in a special "attention layer." Here, the model learns to "focus" on the parts of the image relevant to the question. For example, when asked about the word "table," the model will pay more attention to areas in the image that match the characteristics of a table.
4. Answer Generation: Finally, a large language model (LLM) generates a descriptive text answer based on this fused information.

Simply put, this involves "highlighting" the corresponding parts of the image with words and then describing the "highlighted" parts with words.

2. Project Architecture

Key Code

1. Tool Layer Entry (largemodel/utils/tools_manager.py)

The seewhat function in this file defines the tool's execution flow.

​x
# From largemodel/utils/tools_manager.py
​
class ToolsManager:
    # ...
​
    def seewhat(self):
        """
        Capture camera frame and analyze environment with AI model.
        
        :return: Dictionary with scene description and image path, or None if failed.
        """
        self.node.get_logger().info("Executing seewhat() tool")
        image_path = self.capture_frame()
        if image_path:
            # Use isolated context for image analysis. 
            analysis_text = self._get_actual_scene_description(image_path)
​
            # Return structured data for the tool chain. 
            return {
                "description": analysis_text,
                "image_path": image_path
            }
        else:
            # ... (Error handling)
            return None
​
    def _get_actual_scene_description(self, image_path, message_context=None):
        """
        Get AI-generated scene description for captured image.
        
        :param image_path: Path to captured image file.
        :return: Plain text description of scene.
        """
        try:
            # ... (Build Prompt)
            
 AI large model voice interaction
Multi module visual position application