I propose the concept of the 'Physical Turing Test', where you can't tell if a task was completed by a human or a machine, like cleaning up after a party.

We need to address the challenges of the Physical Turing Test, as current robotics struggle to perform tasks that humans can do effortlessly.

Data collection for robotics is challenging; we can't scrape the necessary data from the internet, and we rely on expensive teleoperation methods to teach robots.

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

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.

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 developed Roboccasta, a large-scale compositional simulation framework that generates 3D assets and environments for robotics tasks, reducing manual effort.

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

The concept of a 'digital cousin' refers to simulations that are not perfect replicas but capture enough detail to be useful, running slower than real-time simulations.

Recent advancements in video generation models have drastically reduced the time needed to simulate complex scenes, demonstrating the power of data-driven processes.

The video shown was entirely generated by a custom model, illustrating the capabilities of fine-tuning general-purpose video generation models on specific domain data.

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.

The video diffusion model compresses hundreds of millions of internet videos into a simulation, enabling robots to interact with objects in a dream-like space.

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.

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

The future of robotics involves a physical API that allows software to manipulate the physical world, similar to how digital APIs work with data.

In the future, robots will be able to perform tasks autonomously, transforming our daily lives by handling chores and responsibilities.

The day we pass the Physical Turing Test will be a significant milestone, marking a shift in how we interact with robots in our everyday lives.