Hardware Requirements
This page outlines the hardware specifications needed for different parts of the course.
Minimum Requirements
For Chapters 1-3 (ROS 2, Gazebo, Unity)
| Component | Minimum | Recommended |
|---|---|---|
| CPU | Intel i5 / AMD Ryzen 5 (4 cores) | Intel i7 / AMD Ryzen 7 (8 cores) |
| RAM | 8GB | 16GB |
| Storage | 50GB free | 100GB+ SSD |
| GPU | Integrated graphics | Dedicated GPU (GTX 1650 or better) |
| OS | Ubuntu 22.04 LTS or Windows 10+ with WSL2 | Ubuntu 22.04 LTS (native) |
Can complete: Chapters 1-3 fully
Recommended Requirements
For Chapters 1-4 (Including NVIDIA Isaac)
| Component | Minimum | Recommended |
|---|---|---|
| CPU | Intel i7 / AMD Ryzen 7 (8 cores) | Intel i9 / AMD Ryzen 9 (16 cores) |
| RAM | 16GB | 32GB+ |
| Storage | 100GB SSD | 250GB+ NVMe SSD |
| GPU | NVIDIA RTX 3060 (6GB VRAM) | NVIDIA RTX 4070 or better (12GB+ VRAM) |
| OS | Ubuntu 22.04 LTS (native) | Ubuntu 22.04 LTS (native) |
Can complete: All chapters including GPU-accelerated Isaac Sim
GPU Requirements Breakdown
Why NVIDIA GPU?
- Isaac Sim: Requires NVIDIA RTX GPU with ray tracing support
- CUDA: GPU-accelerated ML training
- TensorRT: Optimized inference on Jetson devices
GPU Comparison
| GPU Model | VRAM | Isaac Sim | ML Training | Recommended For |
|---|---|---|---|---|
| GTX 1650 | 4GB | ❌ No | Basic | Chapters 1-3 only |
| RTX 3060 | 6GB | ✅ Yes | Moderate | Chapters 1-4 (small scenes) |
| RTX 3070 | 8GB | ✅ Yes | Good | All chapters |
| RTX 4070 | 12GB | ✅ Yes | Excellent | All chapters + complex scenes |
| RTX 4090 | 24GB | ✅ Yes | Outstanding | Professional use |
Cloud Computing Alternatives
If you don't have access to the required hardware, consider:
Google Colab
Pros:
- [SOURCE NEEDED: Colab advantages]
Cons:
- [SOURCE NEEDED: Colab limitations]
Good for: Chapters 1, 2, and ML portions of Chapters 4-5
AWS EC2 with GPU
Pros:
- [SOURCE NEEDED: AWS advantages]
Cost: [SOURCE NEEDED: Pricing estimates]
Good for: All chapters
NVIDIA Omniverse Cloud
Pros:
- [SOURCE NEEDED: Omniverse Cloud advantages]
Good for: Chapter 4 (Isaac Sim)
Physical Robot Hardware (Optional)
For those wanting to deploy on real hardware:
Budget Option: TurtleBot3 Burger
Cost: ~$500 Specs: [SOURCE NEEDED] Good for: Chapters 2-3 sim-to-real
Mid-Range: TurtleBot3 Waffle Pi
Cost: ~$1,800 Specs: [SOURCE NEEDED] Good for: Chapters 2-4
Advanced: NVIDIA Jetson with Custom Robot
Cost: 1,000+ Specs: [SOURCE NEEDED] Good for: Edge deployment (Chapter 4)
Storage Breakdown
| Component | Disk Space |
|---|---|
| Ubuntu 22.04 LTS | ~10GB |
| ROS 2 Humble | ~5GB |
| Gazebo Classic + Models | ~5GB |
| Unity Editor | ~15GB |
| NVIDIA Isaac Sim | ~30GB |
| ML Libraries (PyTorch, TensorFlow) | ~10GB |
| Project Files and Datasets | ~20GB |
| Total | ~95GB |
Recommendation: 150GB+ of fast SSD storage
Checking Your System
CPU Information
# Linux
lscpu
# Windows
wmic cpu get name
RAM Information
# Linux
free -h
# Windows
wmic memorychip get capacity
GPU Information
# Linux (NVIDIA)
nvidia-smi
# Windows (NVIDIA)
nvidia-smi
Disk Space
# Linux
df -h
# Windows
wmic logicaldisk get size,freespace,caption
Upgrade Recommendations
If you're considering upgrading your hardware:
-
Priority 1: GPU - If you have integrated graphics, adding any dedicated GPU will significantly improve performance. For Isaac Sim, prioritize NVIDIA RTX series.
-
Priority 2: RAM - Upgrading from 8GB to 16GB will allow running multiple tools simultaneously (ROS, Gazebo, Unity, IDE).
-
Priority 3: Storage - SSD dramatically improves build times and simulation loading. NVMe is even better.
-
Priority 4: CPU - More cores help with parallel compilation and multiple simulations.
Can I Still Take the Course?
Yes! Even with below-minimum hardware:
- Use cloud platforms for GPU-intensive work (Chapter 4)
- Focus on conceptual learning and run lighter examples
- Use smaller simulation scenes with reduced complexity
- Team up with classmates who have better hardware
Questions? Contact [SOURCE NEEDED: Support information]
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