Overview of Simulation Technology for Advancing Physical AI

The biggest challenge in building Physical AI systems is data availability. Since collecting data in the real world is costly, risky, and slow, physics-based simulation serves as a bridge to solve this problem.

Recently, simulation has become more than just a debugging tool—it has established itself as a core element of the model development loop. Developers use simulation to perform tasks such as:

• Generating perception datasets
• Training reinforcement learning policies
• Augmenting real-world data and benchmarking models
• Testing rare or adversarial scenarios

For efficient Physical AI development, systems are divided into a 3-computer paradigm:

1. Training computer: A large-scale GPU cluster for training foundation models
2. Simulation computer: A workstation/cluster that generates robot experiences and sensor data through GPU-accelerated physics and RTX rendering
3. On-robot computer: An edge device (e.g., NVIDIA Jetson AGX Thor) that deploys and executes trained policies on the actual robot

When choosing a simulation engine, factors such as synthetic data generation workflows, support for reinforcement learning, the range of supported sensors and 3D assets, and environmental fidelity must all be comprehensively considered.