An overview of the current landscape of simulation engines for physical AI and robotics in 2026. Covers why simulation is essential for training physical AI systems given the scarcity of real-world data, and introduces the three-computer paradigm (training, simulation, on-robot). Provides detailed breakdowns of MuJoCo, MuJoCo Warp (MJWarp), NVIDIA Isaac Sim, Isaac Lab 3.0, and the Newton physics engine — a new open-source GPU-accelerated differentiable physics engine developed by NVIDIA, Google DeepMind, and Disney Research. Discusses how Isaac Lab 3.0 decouples from Omniverse to support multiple physics backends. Concludes that open-source, GPU-accessible simulation infrastructure is rapidly becoming foundational to embodied AI development, with the ecosystem converging toward a layered, interoperable stack.
