Everything that moves will be autonomous someday whether partially or fully.
Breakthroughs in AI software has made all kinds of robots possible.
And we are working with companies and enthusiasts all over the world to build these amazing machines
ones that fly, walk, or swim;
with two, four, or more wheels;
underwater or in space;
to deliver pizza, move inventory, farm lettuce and strawberries,
or just being a helping hand.
AI software was the big breakthrough.
But it is the Jetson AI computer that is democratizing robotics.
Jetson is a robotics computer -- it's mighty, yet tiny, energy-efficient, and affordable.
Jetson is an Arm SoC that is designed from the ground up for robotics -- the sensor processors, the CUDA GPU and Tensor Cores, and, most
importantly, the richness of AI software that runs on Jetson.
Today, we are announcing that the Jetson Nano 2GB will be $59. Anyone can now build a robot.
Developing robotics software is like all AI -- a large computer like DGX writes the software
and a small computer like Jetson runs the software.
The AI researchers at NVIDIA have trained many models using our DGX SuperPOD infrastructure
and those pre-trained models are available on NGC.
Using NVIDIA Transfer Learning Tool, you can adapt them to your domain.
But how do we collect the vast diversity of objects, surroundings, and conditions data to train Isaac?
And how do we validate Isaac's functionality in the infinite variability of environments and scenarios that it will operate?
This is where the Isaac simulation comes in.
Isaac Sim is built on Omniverse.
It is a realistic world where we design and train robots.
Isaac Sim imports files using USD Universal Scene Description and URDF,
Unified Robot Description Format, that describe robot models, sensors, and scenes.
This simulation is from the work we're doing with BMW to create a platform to design, simulate, and operate a factory of the future.
The robots are running the actual Isaac stack and interacting with a real-time simulation of the factory and other agents.
This next example is the NVIDIA DRIVE autonomous vehicle system.
Again, the starting point of AI is the DGX training system. This supercomputer learns from data and trains the AI.
The AI software is then OTA'd into the car, powered by the NVIDIA DRIVE AV computer.
Before road testing, our engineers simulate a virtual car in DRIVE Sim, built on Omniverse.
The realism of Omniverse reduces the Sim-to-Real gap and allow our engineers to iterate rapidly,
as if they can instantly update a test car to try their new software build.
Let's take a look:
Hey Mercedes, please pick me up.
Hey Mercedes, please take me for a ride.
Okay, let's start auto-pilot.
Hey Mercedes, please park the car.
Okay, I will go ahead and park the car.
This is work that we are doing for our Mercedes partnership.
Starting with 2024, the entire fleet will be powered by NVIDIA DRIVE AV.
On the left is the RTX server that generates the world view for each sensor.
On the right is the DRIVE AV computer.
The two are connected.
The world views in DRIVE Sim are sent to the DRIVE AV computer, which perceives, localizes, plans,
and actuates the virtual car in DRIVE Sim
This in turn generates new views of the world.
DRIVE AV is running the actual self-driving car stack - all of it - and in theory has no clue it is in a virtual world.
Someday, neither the AI or us will be able to tell whether we are in the virtual or physical world.
DRIVE Sim is an open platform with plug-ins for excellent third-party engines.