Hardware Requirements for Physical AI
To build and deploy the humanoid robotics pipelines described in this book, you need a combination of high-performance simulation workstations and energy-efficient edge compute. This guide specifies the Hackathon-Safe hardware stack, focusing on accessibility, community support, and reliability.
1. Digital Twin Workstation (Simulation)
Simulation platforms like NVIDIA Isaac Sim and Gazebo require dedicated GPU power to handle real-time physics and ray-traced rendering.
| Component | Minimum Specification | Recommended Specification |
|---|---|---|
| GPU | NVIDIA RTX 3060 (8GB VRAM) | NVIDIA RTX 4070 Ti or higher (12GB+ VRAM) |
| CPU | Intel i7 or AMD Ryzen 7 (8 cores) | Intel i9 or AMD Ryzen 9 (16 cores+) |
| RAM | 16 GB DDR4 | 32 GB - 64 GB DDR5 |
| OS | Ubuntu 22.04 LTS (Native) | Ubuntu 22.04 LTS or Windows 11 with WSL2 |
| Storage | 500 GB NVMe SSD | 1 TB NVMe SSD |
For Isaac Sim, VRAM is the primary bottleneck. If your VRAM is < 12GB, you will experience frequent crashes during large-scale RL training. Aim for 16GB-24GB for complex multi-robot simulations.
2. Physical AI Edge Kit (On-Robot Compute)
The "Brain" inside the robot must handle high-DoF control loops and vision inference with minimal latency.
Primary: NVIDIA Jetson Orin Series
The Jetson Orin is the industry standard for edge robotics.
- Jetson Orin Nano: Good for basic navigation and low-res vision.
- Jetson Orin NX (16GB): The "Sweet Spot" for humanoid research.
- Jetson Orin AGX: Required for full VLA (Vision-Language-Action) models running locally.
Secondary: Intel RealSense D435i/D455
The "Eyes" of your robot.
- Function: RGB-Depth perception.
- Setup: Requires the
realsense-rosdriver. - Defensive Tip: Use the D455 for humanoids; its wider field of view helps with balance and foot-placement visualization.
3. Robot Lab Tiers (Hackathon Safe)
We categorize robots into tiers based on cost, complexity, and capability. Use only these platforms for compatibility with this course.
Tier A: Premium Research (Humanoid)
- Platform: Unitree G1 or Unitree H1.
- Cost: $16,000 - $90,000.
- Capability: Full bipedal locomotion, 23-43 DoF, advanced balance recovery.
- Target: Research labs, advanced Capstone projects.
Tier B: Mid-Range Proxy (Quadruped)
- Platform: Unitree Go2 or Unitree AlienGo.
- Cost: $2,000 - $15,000.
- Capability: 4-legged stability, excellent for testing navigation and VLA before humanoid transfer.
- Target: Hackathon teams, small labs.
Tier C: Economy / Educational (Miniature)
- Platform: Hiwonder TonyPi or Robotis OP3.
- Cost: $500 - $10,000.
- Capability: Simplified kinematics, great for learning ROS 2 and basic gait control.
- Target: Beginners, individual learners.
4. Architecture Summary Table
| Layer | Component | Hardware Requirement | Function |
|---|---|---|---|
| Cognitive | LLM / VLA | RTX 4090 or Cloud (A100) | High-level reasoning and planning |
| Perception | Vision / VSLAM | Jetson Orin + RealSense | 3D Mapping and object detection |
| Control | RL Policy | Jetson Orin (Low-latency) | 500Hz balance and torque control |
| Actuation | Joint Motors | Unitree Dual-Motor | Physical movement execution |
5. Cloud OpEx & Economy Kit
Cloud-Native Options (Non-RTX Users)
If you lack a powerful local GPU, you can use cloud instances:
- AWS RoboMaker: ~ $2.50 / hour.
- Lambda Labs (RTX 6000): ~ $0.80 / hour.
- Estimated Cloud OpEx: ~ $200 - $500 per quarter for active development.
The "Economy Kit" Setup
For individual learners on a budget:
- Compute: Refurbished RTX 3060 PC ($600).
- Edge: Jetson Orin Nano Developer Kit ($499).
- Robot: Hiwonder TonyPi ($500).
- Total: ~ $1,600 for a complete Physical AI lab.
6. The Latency Trap & Solution
The Trap: Sending sensor data from the Jetson to a Cloud LLM and waiting for a motor command takes 500ms - 2000ms. In this time, a humanoid will fall.
The Defensive Solution:
- Local Safety Envelope: Always run the 500Hz balance loop LOCALLY on the Jetson.
- Asynchronous Reasoning: The Cloud LLM sends "Goals" (e.g., "Walk to kitchen"), while the local Jetson handles "Steps" (e.g., "Keep balancing while walking").
- Heartbeat Watchdog: If the cloud connection drops, the local controller must transition to a safe "Stand-still" or "Sit" state immediately.
Summary: Your hardware is the foundation of your Physical AI. Don't cut corners on VRAM or cooling. A stable workstation leads to a stable robot.