🤖 AI Summary
This work addresses the challenges of achieving low-latency, multilingual voice interaction and cross-platform task automation for smart glasses in real-world scenarios. The authors propose an edge-oriented dual-agent collaborative architecture: Agent 01 handles multilingual speech recognition, while Agent 02 leverages a local large language model integrated with the MCP protocol, retrieval-augmented generation (RAG), and external tools to perform task reasoning and execution. The system supports RTSP audio-video streaming, eye-tracking data acquisition, and RabbitMQ-based remote communication, enabling end-to-end real-time voice command understanding and cross-platform task orchestration. Experimental results demonstrate the feasibility of deploying such a sophisticated AI agent system on resource-constrained wearable devices, significantly enhancing both interactive efficiency and multilingual adaptability.
📝 Abstract
This paper presents an AI glasses system that integrates real-time voice processing, artificial intelligence(AI) agents, and cross-network streaming capabilities. The system employs dual-agent architecture where Agent 01 handles Automatic Speech Recognition (ASR) and Agent 02 manages AI processing through local Large Language Models (LLMs), Model Context Protocol (MCP) tools, and Retrieval-Augmented Generation (RAG). The system supports real-time RTSP streaming for voice and video data transmission, eye tracking data collection, and remote task execution through RabbitMQ messaging. Implementation demonstrates successful voice command processing with multilingual support and cross-platform task execution capabilities.