6.1 Integrating GPT models for conversational AI in robots
Topic: Integrating GPT Models for Conversational AI in Robots
📘 Chapter: Conversational AI with GPT in Humanoid Robots
🔹 Introduction
Conversational AI allows humanoid robots to understand and respond to human language naturally. By integrating GPT models, robots can process speech, text, and intent, enabling intelligent and context-aware conversations.
Humanoid robots equipped with GPT-powered conversational AI can:
- Answer questions
- Give instructions
- Engage in social interaction
- Assist in tasks
🧠 1. Overview of GPT Models for Robotics
What GPT Provides:
- Language understanding (NLU)
- Context-aware responses
- Text generation and summarization
- Multi-turn conversation handling
Why GPT for Robots?
- Scalable: Single model can handle multiple tasks
- Flexible: Can understand varied user inputs
- Integratable: Works with Python, ROS, cloud APIs
🧠 2. Speech Recognition and NLU Pipeline
┌───────────────────────────────┐
│ 1. Microphone / Audio Input │
└─────────────┬─────────────────┘
│
v
┌───────────────────────────────┐
│ 2. Speech-to-Text Engine │
│ (Google STT / Whisper) │
└─────────────┬─────────────────┘
│
v
┌───────────────────────────────┐
│ 3. GPT Model (NLU + Response) │
│ - Understand context │
│ - Generate text response │
└─────────────┬─────────────────┘
│
v
┌───────────────────────────────┐
│ 4. Text-to-Speech (TTS) │
│ - Convert GPT text to speech │
└─────────────┬─────────────────┘
│
v
┌───────────────────────────────┐
│ 5. Humanoid Robot Response │
│ - Mouth / Speaker / Gestures │
└───────────────────────────────┘
🌀 3. Workflow for GPT Integration in Humanoids
- Audio Capture: Robot uses microphone to record user speech
- Speech-to-Text: Converts audio to text
- GPT Processing:
- Processes text
- Understands intent & context
- Generates conversational response
- Text-to-Speech: Converts GPT output to audio
- Robot Actuation: Robot speaks and optionally moves body/gestures
🤖 4. Implementation Components
Core Modules:
- Speech Recognition: Whisper, Google STT, or Azure Speech
- GPT Model: OpenAI GPT API / Local GPT Model
- Text-to-Speech: Coqui TTS, gTTS, or Azure TTS
- Robot Control Interface: ROS2 / Python SDK for humanoid
Integration Example (Python Pseudo Code)
# Speech-to-Text
user_input = speech_to_text(microphone)
# GPT Response
response_text = gpt_model.generate(user_input)
# Text-to-Speech
audio_output = text_to_speech(response_text)
# Play audio on robot
robot.speaker.play(audio_output)
🏭 5. Applications
- Customer service humanoids
- Companion robots for elderly or children
- Educational humanoid robots
- Interactive guides in museums or events
- Assistive robots in homes or hospitals
📝 6. Self Assignment
Tasks:
- Setup a GPT API account (OpenAI / local GPT)
- Use microphone to capture speech input
- Convert speech to text (STT engine)
- Send text to GPT and receive response
- Convert GPT response to speech and play it
- Add simple gestures for robot when replying
❓ 7. MCQs (Multiple Choice Questions)
Q1. GPT in robots is used for?
A. Battery optimization
B. Language understanding & response generation
C. Mechanical control
D. Temperature sensing
✔ Correct Answer: B
Q2. Which module converts speech to text?
A. Text-to-Speech
B. GPT Model
C. Speech Recognition Engine
D. ROS2
✔ Correct Answer: C
Q3. Text-to-Speech (TTS) does?
A. Converts robot commands to code
B. Converts GPT text response to audio
C. Generates visual animations
D. Measures robot velocity
✔ Correct Answer: B
Q4. Which interface is commonly used to control humanoid robots?
A. Microsoft Excel
B. ROS2 / Python SDK
C. Unity only
D. Linux Terminal only
✔ Correct Answer: B
Q5. What is the main benefit of GPT in humanoids?
A. Makes robots walk faster
B. Enables context-aware conversation
C. Charges robot battery
D. Improves camera resolution
✔ Correct Answer: B
This chapter explains Integrating GPT Models for Conversational AI in humanoid robots with workflow, practical example, and self-assignment exercises.