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.

We are committed to driving innovation in robotics by combining AI with robotics, which is where the next significant advancements will occur.

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.

I believe that ideas from classical artificial intelligence can be integrated into modern deep reinforcement learning systems to enhance their capabilities.

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.

We are at an exciting moment where advancements in AI algorithms are finally enabling robotics to realize its potential.

AI advancements are crucial for the future of robotics, as they enable us to build models that work safely and effectively in real-world scenarios.

Robotics is progressing rapidly, and with the integration of AI, we can expect robots to communicate and interact with humans more effectively.

In the next year, I hope to see advancements in robotics as a new model capability.