Skip to main content

Part 6: Conversational Robotics

Welcome to Part 6! Your humanoid can walk, balance, and manipulate objects. Now it's time to give it a voice, vision-language understanding, and the ability to interact naturally with humans. This is where Physical AI meets modern LLMs and multi-modal AI.

The Interaction Revolution​

Traditional Robotics: Programmed behaviors, predefined commands, expert operators Conversational Robotics: Natural language instructions, vision-language grounding, intuitive gestures

Industry Shift: Every major humanoid (Tesla Optimus, Figure 01, 1X NEO) emphasizes natural interaction as a key differentiator. The robot that understands "Bring me the blue mug from the kitchen table" without explicit programming will dominate.

What You'll Learn in Part 6​

Chapter 18: Natural Language Processing (3 lessons)​

Enable your robot to understand spoken commands, detect user intent, and manage multi-turn dialogues using modern NLP and LLMs.

Key Topics:

  • Speech recognition with Whisper (OpenAI's robust ASR)
  • Intent detection and slot filling (BERT, RoBERTa)
  • Dialogue management with LLMs (GPT-4, Claude integration)
  • ROS 2 NLP pipelines
  • Real-time speech-to-text latency optimization

Implementation: Voice-controlled robot with "Bring me X" command understanding

Real-World Use: Every conversational humanoid (Optimus, Figure 01, NEO)

Chapter 19: Vision-Language Models (3 lessons)​

Bridge vision and language with CLIP, Grounding DINO, and robotics VLMs (RT-2, PaLM-E). Enable robots to understand "the red cup on the left" without object-specific training.

Key Topics:

  • CLIP embeddings for image-text alignment
  • Grounding DINO for open-vocabulary object detection
  • Vision-language grounding (text → pixel locations)
  • RT-2 and PaLM-E for robot control from language
  • Integration with robot manipulation pipelines

Breakthrough: Zero-shot object manipulation with natural language

Real-World Impact: Google's RT-2 robot manipulation, OpenAI's VLA research

Chapter 20: Gesture Recognition (3 lessons)​

Enable intuitive human-robot interaction through gestures. Recognize hand poses, body language, and pointing gestures using MediaPipe and temporal models.

Key Topics:

  • Pose estimation with MediaPipe and OpenPose
  • Hand tracking for gesture classification
  • Temporal gesture recognition (LSTM, Transformers)
  • Mapping gestures to robot actions
  • Deictic gestures (pointing) for object reference

Use Cases: "Bring this" (pointing), waving hello, stop/go gestures

Industry Application: Warehouse robots, collaborative manufacturing

Chapter 21: Real-Time Interaction (3 lessons)​

Integrate speech, vision, and gestures into fluid, real-time interaction systems. Optimize for low latency, implement multi-modal fusion, and design socially-aware robot behaviors.

Key Topics:

  • Latency optimization (pipeline parallelization, buffering)
  • Multi-modal fusion (combine speech + vision + gesture)
  • Turn-taking and interruption handling
  • Social navigation (proxemics, personal space)
  • Attention mechanisms (where should the robot look?)

Performance Target: <300ms end-to-end latency for natural interaction

Real-World Standard: Human conversation latency ~200-300ms

Learning Approach​

Part 6 is integration-focused and LLM-heavy. You'll:

  • Integrate modern LLMs (GPT-4, Claude, open-source alternatives)
  • Deploy vision-language models (CLIP, Grounding DINO, RT-2)
  • Build real-time pipelines with ROS 2
  • Optimize for latency (critical for natural interaction)
  • Test on humanoid platforms from Part 5

Prerequisites​

Before starting Part 6:

  • ✅ Complete Parts 2-5 (ROS 2, Isaac, humanoid control)
  • ✅ Familiarity with transformer models (BERT, GPT architecture)
  • ✅ Python with PyTorch or TensorFlow
  • ✅ API access to LLMs (OpenAI, Anthropic, or open-source)
  • ✅ Basic understanding of multi-modal AI concepts

Development Environment​

You'll need:

  • LLM Access: OpenAI API, Anthropic Claude, or local (Llama, Mistral)
  • Vision Models: CLIP, Grounding DINO (Hugging Face)
  • Speech: Whisper (OpenAI), Vosk (offline alternative)
  • Gesture: MediaPipe, OpenPose
  • ROS 2 Humble with Isaac Sim integration
  • NVIDIA GPU (RTX 3060+ for VLM inference)

Estimated Time​

⏱️ Total Time for Part 6: 18-24 hours

  • Core lessons: 12-15 hours (12 lessons × 1-1.25 hours each)
  • Integration work: 4-6 hours (building interaction pipelines)
  • Optimization and testing: 2-3 hours (latency tuning)

Recommended Pace: 2 lessons per week over 6 weeks

Part Structure​

Chapter 18: Natural Language Processing (3 lessons)

  1. Speech Recognition (Whisper)
  2. Intent Detection (BERT, slot filling)
  3. Dialogue Management (LLM integration)

Chapter 19: Vision-Language Models (3 lessons)

  1. CLIP Embeddings (image-text alignment)
  2. VLM Grounding (Grounding DINO, SAM)
  3. Robotics VLM (RT-2, PaLM-E, VLA)

Chapter 20: Gesture Recognition (3 lessons)

  1. Pose Estimation (MediaPipe, OpenPose)
  2. Gesture Classification (temporal models)
  3. Gesture to Robot Control (action mapping)

Chapter 21: Real-Time Interaction (3 lessons)

  1. Latency Optimization (parallelization)
  2. Multi-Modal Fusion (speech + vision + gesture)
  3. Social Navigation (proxemics, attention)

Connection to Other Parts​

Building on Parts 2-5:

  • ROS 2 proficiency → Implements interaction pipelines
  • Isaac perception → Provides visual input for VLMs
  • Humanoid control → Executes commands from language/gestures

Preparing for Part 7:

  • Interaction skills → Required for capstone demo
  • System integration → Complete robot system
  • Real-world validation → Human-robot interaction testing

The Vision-Language Revolution​

2023-2024 Breakthroughs:

  • RT-2 (Google): Vision-language-action models for manipulation
  • PaLM-E (Google): Embodied multi-modal language model
  • Grounding DINO: Open-vocabulary object detection
  • SAM (Meta): Segment Anything for vision grounding

Result: Robots can now:

  • Understand "Bring me the red mug" without red-mug-specific training
  • Ground abstract concepts ("Something to write with") to objects (pen, pencil)
  • Learn manipulation from language descriptions
  • Zero-shot generalization to new objects and tasks

Industry Context​

Companies Leading Conversational Robotics:

  • Figure AI: Figure 01 with OpenAI language integration
  • Tesla: Optimus voice control
  • 1X Technologies: NEO conversational assistant
  • Covariant: Warehouse robots with language-guided picking
  • Google DeepMind: RT-2 and robotics transformers

Market Drivers:

  • Accessibility: Non-experts can operate robots
  • Flexibility: One robot, many tasks via language
  • Safety: Natural interaction reduces errors
  • Adoption: Conversational interfaces lower training costs

Real-Time Performance Requirements​

Latency Budget (300ms total for natural interaction):

  • Speech-to-Text: 100-150ms (Whisper optimized)
  • Intent Detection: 20-30ms (BERT inference)
  • LLM Planning: 50-100ms (GPT-4 turbo or local)
  • Robot Execution: 50-100ms (motion planning)

Optimization Strategies:

  • Pipeline parallelization (overlap speech and vision)
  • Predictive processing (anticipate user intent)
  • Caching (common commands pre-computed)
  • Edge deployment (on-robot inference)

Success Criteria​

By the end of Part 6, you will be able to:

✅ Implement robust speech recognition with Whisper ✅ Detect user intent and extract task parameters from language ✅ Integrate LLMs (GPT-4, Claude) into robot dialogue systems ✅ Deploy vision-language models for open-vocabulary object detection ✅ Ground natural language to visual percepts (pixel-level localization) ✅ Implement gesture recognition with MediaPipe ✅ Map gestures to robot actions ✅ Build low-latency interaction pipelines (<300ms) ✅ Fuse multi-modal inputs (speech + vision + gesture) ✅ Design socially-aware robot navigation and attention ✅ Validate interaction quality with human testers

What Comes Next​

After completing Part 6, you'll move to Part 7: Capstone Project, where you'll:

  • Design and implement a complete humanoid system
  • Integrate perception, planning, control, and interaction
  • Deploy on simulated humanoid platforms (Isaac Sim)
  • Document your system architecture
  • Present your work (video demo, technical report)

Part 7 is your opportunity to showcase mastery of the entire Physical AI stack.

Ready for conversational robotics? Begin with Chapter 18: Natural Language Processing

Part 6 is Week 11 of the 13-week curriculum.