AI & Robotics Simulation Engineering Lab – Detailed Curriculum (60 Days)
Week 1: Foundations & Lab Orientation
- Program orientation, expectations, and rules (not for hobby/family learning)
- Linux, Git, and engineering workflows
- Introduction to simulation-first robotics
- Understanding digital twins vs physical-first robotics
- Vibe Coding methodology and responsible AI usage (Claude)
Week 2: NVIDIA Isaac Sim Fundamentals
- Isaac Sim architecture and Omniverse basics
- Scene creation, assets, and USD concepts
- Physics engines, sensors, and robot models
- Running baseline robot simulations
- Debugging simulation issues
Week 3: Mobile Robots & Navigation
- Differential drive and kinematic models
- Sensor simulation: LiDAR, camera, IMU
- Localization concepts in simulation
- Navigation stacks and behavior trees
- Failure scenarios and recovery logic
Week 4: Autonomous Behavior & AI Integration
- Perception pipelines in simulation
- AI-based obstacle detection and decision making
- Reinforcement learning concepts (intro)
- Metrics, logging, and performance evaluation
- Design defense: explain why your robot behaves as it does
Week 5: Drones & Industrial Digital Twins
- Drone simulation fundamentals
- Autonomous flight, tracking, and avoidance
- Industrial robots and factory digital twins
- Inspection and monitoring use cases
- Safety constraints and validation in simulation
Week 6: Sim-to-Real Thinking & Capstone
- Simulation-to-real gaps and mitigation strategies
- Jetson Orin and Pixhawk deployment considerations
- Capstone project definition and execution
- Live simulation demo and failure handling
- Final evaluation, scoring, and certification
Assessment & Evaluation
- Architecture quality and reasoning
- Correctness of simulation and autonomy logic
- Ability to debug failures and iterate
- Final capstone demo and design defense
Outcome
Graduates of this lab will be able to design, simulate, validate, and explain autonomous robotic systems using modern NVIDIA simulation tools.