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Conversational AI

Conversational AI enables robots and systems to understand, process, and respond to natural language. By combining speech recognition, natural language understanding, and dialogue management, robots can interact with humans intelligently and naturally.

This lesson introduces the fundamentals, architecture, and ROS 2 integration of Conversational AI in robotics.

Learning Objectives​

By the end of this lesson, students will be able to:

  • Understand what Conversational AI is
  • Explain speech-to-text and text-to-speech systems
  • Understand Natural Language Understanding (NLU) and Dialogue Management
  • Integrate Conversational AI with ROS 2 Humble
  • Enable humanoid robots to receive and respond to verbal instructions
  • Implement simple voice-controlled tasks in simulation or real robots
  • Understand the use of AI and ML models for human–robot interaction

Prerequisites​

  • Completed Week 1-8: ROS 2, simulation, and sensor fundamentals
  • Completed Week 9: Vision-Language-Action Models
  • Python 3.8+ with speech recognition libraries
  • Basic understanding of Natural Language Processing (NLP)
  • Familiarity with audio processing and speech APIs
  • ROS 2 Humble workspace configured
  • Microphone and audio output device for testing

1. What is Conversational AI?​

Conversational AI is a technology that allows robots to:

  • Listen and understand human speech
  • Process instructions or queries
  • Respond intelligently
  • Engage in multi-turn conversations

✅ Example:

  • Human: “Bring me the red cube.”
  • Robot: Detects red cube → Grasps → Moves to human → Confirms: “Here is the red cube.”

2. Importance of Conversational AI in Robotics​

Conversational AI enables:

  • Hands-free robot control
  • Human–robot interaction (HRI)
  • Smart assistants and service robots
  • Accessibility for disabled users
  • Multi-modal AI integration (vision + language + action)

✅ Humanoid and service robots rely heavily on conversational AI for effective interaction.

3. Architecture of Conversational AI​

1. Speech Recognition​

  • Converts spoken language into text
  • Examples: Google Speech-to-Text, Whisper

2. Natural Language Understanding (NLU)​

  • Parses text into structured data
  • Identifies intents and entities

3. Dialogue Management​

  • Determines appropriate responses or actions
  • Can be rule-based or AI-based

4. Text-to-Speech (TTS)​

  • Converts robot response text into speech
  • Examples: Google TTS, Amazon Polly

✅ End-to-end workflow:
Human speech → ASR → NLU → Dialogue Manager → TTS → Robot speech/action

4. ROS 2 Integration​

Conversational AI can be integrated with ROS 2 using:

  • ROS 2 topics:
    • /voice_input → Publishes user speech
    • /voice_output → Robot’s speech
    • /robot_cmd → Commands for motion
  • ROS 2 nodes:
    • ASR Node (speech recognition)
    • NLU Node (intent parsing)
    • Action Node (robot motion execution)
    • TTS Node (robot response)

✅ Enables real-time human–robot interaction.

5. Practical Examples​

Example 1: Voice-Controlled Robot​

  • Human: “Move forward 1 meter”
  • ASR converts speech to text
  • NLU identifies intent: move
  • Action node executes /cmd_vel command
  • Robot moves forward

Example 2: Query-Based Assistance​

  • Human: “Where is the red cube?”
  • Robot uses camera vision + NLP to answer
  • Robot responds: “The red cube is on the table”

Example 3: Multi-Turn Conversation​

  • Human: “Pick up the cube” → Robot: “Which cube?”
  • Human: “The red one” → Robot executes action

6. Tools & Technologies Used​

  • ROS 2 Humble
  • Python / C++
  • Speech-to-Text APIs (Google, Whisper)
  • Text-to-Speech APIs (Google TTS, Amazon Polly)
  • NLP Libraries (Rasa, Hugging Face Transformers)
  • OpenCV (optional, for vision)
  • Unity / Gazebo / Isaac Sim (for simulation)

🧪 7. Hands-On Exercises (Coming Soon)​

✅ Setup ASR node in ROS 2
✅ Convert speech to text and publish to topic
✅ Parse intents using NLP
✅ Execute simple robot actions via voice commands
✅ Implement basic TTS responses
✅ Simulate multi-turn conversations

8. Knowledge Check Quiz (Coming Soon)​

  • What is Conversational AI?
  • Difference between ASR and TTS?
  • How does NLU interact with ROS 2 nodes?
  • Example of a humanoid robot task using conversational AI

9. Glossary​

  • ASR: Automatic Speech Recognition
  • NLU: Natural Language Understanding
  • TTS: Text-to-Speech
  • ROS 2 Topic: Data communication channel
  • Dialogue Manager: Determines robot response/action
  • Intent: Meaning extracted from user speech

10. Further Reading (Coming Soon)​

  • ROS 2 Speech Recognition Tutorials
  • Conversational AI with Python & NLP
  • Human–Robot Interaction Research Papers
  • TTS & ASR API Documentation
  • Multi-modal AI in Robotics

Lesson Summary​

This lesson introduced Conversational AI and its integration with ROS 2 Humble. Students learned how speech recognition, natural language understanding, and dialogue management enable humanoid and autonomous robots to interact intelligently with humans. Practical applications include voice-controlled movement, query-based assistance, and multi-turn conversations.

📌 This lesson prepares students for advanced human–robot interaction, voice-controlled robotics, and AI-integrated autonomous systems.

Version: ROS 2 Humble
License: CC BY-SA 4.0