The State of Simulation for Physical AI: An Overview

Physical AI systems like robots need large amounts of training data, but collecting this data in the real world is slow, expensive, and risky. Simulation engines bridge this gap by generating synthetic data through GPU-accelerated physics, allowing developers to create thousands of hours of robot experience at a fraction of real-world costs. Different simulation engines exist for different purposes, such as reinforcement learning, synthetic data generation, photorealistic rendering, and contact-rich physics, so developers must choose based on their specific needs around sensor support, asset formats, environmental fidelity, and scalability requirements.