To train robots effectively, we must simulate environments at 10,000 times faster than real-time and vary parameters like gravity and friction to ensure adaptability.

In simulation, we can replay trajectories and vary motions without needing to demonstrate them repeatedly with a real robot, enhancing efficiency in training.

By training neural networks in diverse simulated environments, we can prepare them to handle real-world scenarios, effectively creating a digital twin of the robot and its environment, as demonstrated by humanoid robots that trained for 10 years in just two hours of simulation time to learn walking, showcasing the efficiency of simulation in robotics training.

The combination of classical simulation and neural networks will provide the necessary power to advance the next generation of robotics systems.

While a 10 million GPU synchronous pre-training run may not look like current methods, there will likely be some form of training run at that scale in the future, and I would call the learning process semi-synchronous, as it involves many GPUs working together on an AI system, but not all parts will necessarily communicate with each other.

In robotics, clear perception of the environment enhances performance, leading us to conduct experiments with both real and simulated robots.

The digital twin paradigm allows for high-speed simulations, but creating a digital twin requires tedious manual work to build the robot and environment.

By prompting the model with different language, we can simulate counterfactuals and complex scenes, allowing for the generation of varied scenarios based on the same initial conditions and enabling a robot to interact with objects as if it were in the real world, even if the hardware doesn't support it.

As we scale up classical simulations, we will encounter limitations due to the handcrafted nature of the system, which is why neural world models will exponentially enhance our capabilities.