By running 10,000 simulations in parallel, we can transfer training to real robots without any fine-tuning, showcasing the power of simulation in robotics.

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.

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.

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.

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

I believe that AI can learn effectively through simulation and real-life experiences, and it doesn't necessarily need to mimic human physical forms to be effective.

We can track any pose and follow any velocity vector, addressing the complex whole body control problem of humanoid 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.

We aim to reduce the time it takes to train models, allowing engineers to conduct multiple experiments simultaneously and improve workflow efficiency.