🚀 Core Technologies
Six pillars that form the complete Physical AI stack — from hardware to simulation to deployment.
Edge AI
Deploy AI models on resource-constrained devices like NVIDIA Jetson Orin Nano with real-time inference.
- NVIDIA Jetson Platform
- TensorRT Optimization
- Real-time Inference
RAG Systems
Build intelligent chatbots with Retrieval-Augmented Generation using vector databases and LLMs.
- Qdrant Vector DB
- Grok LLaMA 3.3 70B
- Hardware-Aware Context
Sim-to-Real
Bridge the gap between simulation and reality — deploy policies trained in Gazebo to real robots.
- Gazebo Simulation
- Unitree Go1 Robot
- Policy Transfer
ROS 2 & Gazebo
Master the Robot Operating System 2 for building modular, production-grade robotic applications.
- ROS 2 Humble
- Nav2 Stack
- Custom Packages
NVIDIA Isaac
Use NVIDIA Isaac Sim for physics-accurate robot simulation and synthetic data generation.
- Isaac Sim
- Synthetic Data
- GPU Acceleration
Conversational AI
Give robots a voice — build humanoid conversational interfaces with real-time speech & NLP.
- Speech-to-Text
- LLM Reasoning
- Text-to-Speech
🗺️ Your Learning Journey
Four milestones from zero to deploying AI on a real robot.
Hardware Setup
Configure your hardware profile — RTX GPU, Jetson Orin, or Unitree robot. The curriculum adapts to what you have.
Configure →Learn Foundations
Start with Physical AI fundamentals, ROS 2 basics, and Python robotics — weeks 1–4 build your core skills.
Start Week 1 →Build RAG Pipeline
Wire up Qdrant + Grok LLaMA to build a chatbot that knows your robot docs — weeks 5–9.
View Roadmap →Deploy to Robot
Take your trained policies from Isaac Sim to a real Unitree Go1 — the full Sim-to-Real pipeline.
See Weeks 10–13 →Ready to build the future?
Join thousands of engineers mastering Physical AI — start free, no hardware required for the first 4 weeks.