Simulation for physical AI systems relies on generating large amounts of physically grounded data, which is challenging to collect in the real world due to safety, cost, and practicality concerns. Simulation engines like MuJoCo, Isaac Sim, and others can generate photorealistic data using GPU parallelism, enabling developers to train reinforcement learning policies and test policies against rare scenarios. The choice of simulation engine depends on factors such as scalability, sensor support, 3D asset formats, and environmental fidelity required for the specific use case. AI summary
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