An LLM-powered robot control system that enables natural language interaction with industrial robots through voice commands or text input.

The system follows a 5-step pipeline architecture that processes human voice commands into precise robot actions:

1. Voice Activity Detection (VAD): Captures and filters human speech from voice instructions.
2. Speech-to-Text Transcription: Uses Gemini-Flash-2.5 for automatic speech recognition, converting filtered audio to text tokens.
3. AI Agent Processing: The agent processes text input, maintains conversation memory, and generates appropriate robot tasks.
4. Robot Control Interface: Python-based control tools execute tasks and provide real-time feedback to the agent.
5. Text-to-Speech Response: Azure TTS converts the agent's responses back to speech for seamless human interaction.

This architecture enables bidirectional communication between human and robot through natural language, with persistent memory for context-aware conversations.

@article{KADRI2025106660,
title = {LLM-driven agent for speech-enabled control of industrial robots: A case study in snow-crab quality inspection},
journal = {Results in Engineering},
volume = {27},
pages = {106660},
year = {2025},
issn = {2590-1230},
doi = {https://doi.org/10.1016/j.rineng.2025.106660},
url = {https://www.sciencedirect.com/science/article/pii/S2590123025027276},
author = {Ibrahim Kadri and Sid Ahmed Selouani and Mohsen Ghribi and Rayen Ghali and Sabrina Mekhoukh},
keywords = {Large language models (LLMs), Voice interface, KUKA industrial robot, Human-robot interaction, Autonomous robotic planning, Computer vision},}

1. Clone the repository:

   git clone https://github.com/Rayen023/RobotVoiceControl.git
   cd RobotVoiceControl

• Project Structure:

  RobotVoiceControl/
  ├── voice_agent.py                # Voice-controlled interface
  ├── text_agent.py                 # Text-based interface
  ├── src/
  │   ├── agent_common.py           # Shared agent configuration
  │   ├── agent_tools.py            # Robot control tools and functions
  │   ├── robot_control.py          # Core robot communication
  │   ├── tts.py                    # Text-to-speech implementation
  │   ├── simple_emotion_detector.py# Emotion recognition
  │   └── tech_doc.md               # RAG Technical documentation for 
  └── pyproject.toml                # Project dependencies
  

2. Install dependencies using uv:

   uv sync

3. Configure environment variables: Create a .env file in the root directory with the following API keys:

   # Azure Speech Services (for Text-to-Speech)
   AZURE_SUBSCRIPTION_KEY=your_azure_subscription_key
   AZURE_REGION=your_azure_region
   
   # Google GenAI (for Speech-to-Text)
   GOOGLE_API_KEY=your_google_api_key
   
   # OpenRouter (for the main AI agent)
   OPENROUTER_API_KEY=your_openrouter_api_key
   OPENROUTER_BASE_URL=https://openrouter.ai/api/v1

Voice Control Mode:

python voice_agent.py

Text Control Mode:

python text_agent.py

• Natural Language Processing: Understands robot commands in conversational language.
• Context Awareness: Maintains conversation history and robot state.
• Multi-modal Input: Supports both voice and text commands.
• Command Translation: Converts natural language to robot instructions.
• Error Recovery: Robust error handling and automatic retry mechanisms.
• Modular Architecture: Easily integrates new tools and capabilities.
• Real-time Feedback: Provides live position monitoring and status updates.

The system establishes communication with the robot using:

• Primary Communication: Telnet connection for sending KRL (KUKA Robot Language) commands.
• Monitoring: py-openshowvar library for reading robot variables and positions.
• Vision Integration: HTTP/FTP communication with Cognex vision systems.

The system generates and executes KRL commands for:

• Position Control: Cartesian and joint space movements (linear and point-to-point).
• Joint Movements: Precise angular positioning with real-time feedback.
• Pick and Place Operations: Automated object manipulation with gripper control.
• Home Position Initialization: Safe startup and reference positioning.
• Real-time Position Monitoring: Continuous status updates and position tracking.
• Safety Monitoring: Real-time position feedback and collision avoidance.
• Vision Integration: Object detection via Cognex cameras.