As collecting real-world robotics data remains expensive and slow, developers increasingly rely on GPU-accelerated simulators to generate synthetic training data at scale. The piece surveys major simulation frameworks including MuJoCo, Isaac Sim, and Isaac Lab, noting each addresses different needs such as reinforcement learning, photorealistic rendering, or contact-rich physics. It highlights Newton, a new open-source, GPU-accelerated, differentiable physics engine that represents a shift toward modular simulation infrastructure enabling high-throughput robot learning on consumer GPUs.
