AiNex

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This Python-based MCP server transforms the Baby Brewie robot into an intuitive, voice-controlled system. It integrates a dedicated voice agent for wake-word activation and speech recognition, sending commands to a large language model (LLM) via the Together API. The server also provides the LLM with context from pre-defined robot action groups, allowing for intelligent execution of complex tasks. Responses are voiced using gTTS and cached for reduced latency, while robust ROS communication ensures seamless robot interaction.

Características Principales

• Robust ROS Communication: Employs a rewritten WebSocket manager based on roslibPY for reliable ROS message passing using JSON.
• Contextual Action Execution: Passes pre-defined robot action groups to the LLM, enabling intelligent, context-aware command execution without exact naming.
• LLM Integration: Connects with large language models (e.g., Together AI) to interpret natural language commands and control robot actions.
• Extensible Toolset: Provides additional MCP functions like image capture, precise step control, and named action execution.
• Advanced Voice Control: Features wake-word activation (Porcupine), speech recognition (SpeechRecognition), LLM interaction, and voiced responses (gTTS) with caching.
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Casos de Uso

• Automating complex robot tasks through intelligent contextual interpretation of user requests.
• Operating humanoid robots using natural spoken language commands.
• Developing advanced human-robot interaction systems powered by generative AI.