The Future of Robotics: Innovations and Industry Insights

let's go talk about robotics shall [Music] we let's talk about

robots well the time has come the time have has come for robots uh robots have the benefit the benefit of being able to

interact with the physical world and do things that otherwise digital information cannot uh we know very

clearly that the world is has severe shortage of of human labors human workers by the end of this decade the

world is going to be at least 50 million workers short we'd be more than delighted to pay them each $50,000 to

come to work we're probably going to have to pay robots $50,000 a year to come to work and so this is going to be

a very very large industry there are all kinds of robotic systems your infrastructure will be robotic billions

of cameras and warehouses and factories 10 20 million factories around the world every car is already a robot as I

mentioned earlier and then now we're building general robots let me show you how we're doing that

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are moving so fast everybody pay attention to this space this could very well likely be the largest industry of

all at its core we have the same challenges as I mentioned before there are three that we focus on they are

rather systematic one how do you solve the data problem how where do you create the data

necessary to train the AI two what's the model architecture and then three what's the scaling loss how can we

scale either the data the compute or both so that we can make AIs smarter and smarter and smarter how do we scale and

those two those fundamental problems exist in robotics as well in robotics we created a system called

Omniverse it's our operating system for physical AI you've heard me talk about Omniverse for a long time we added two

technologies to it today I'm going to show you two things one of them is so that we could scale AI with generative

capabilities a generative model that understand the physical world we call it cosmos using

Omniverse to condition Cosmos and using Cosmos to generate an infinite number of environments allows us to create data

that is grounded grounded controlled by us and yet be systematically infinite at the same time okay so you see Omniverse

we use Candy Colors to give you an example of us controlling the robot in the scenario perfectly and yet O Cosmos

can create all these virtual environments the second thing just as we were talking about earlier one of the

incredible scaling capabilities of language models today is reinforcement learning verifiable rewards the question

is what's the verifiable rewards in robotics and as we know very well it's the laws of physics verifiable physics

rewards and so we need an incredible physics engine well most physics engines have been designed for a variety of

reasons it could be designed because if we want to use it for large machineries or uh maybe we design it for uh virtual

worlds video games and such but we need a physics engine that is designed for very fine

grain rigid and soft bodies designed for being able to train tactile feedback and fine motor skills and actuator controls

we needed to be GPU accelerated so that we these virtual worlds could live in super linear time super real time and

train these AI models incredibly fast and we needed to be integrated harmoniously into a framework that is

used by roboticists all over the world Mujoko and so today we're announcing something really really special it is a

partnership of three companies Deep Mind Disney Research and Nvidia and we

call it Newton let's Let's take a look at Newton [Music]

[Music] [Applause] [Music]

[Applause] thank you all right let's start that over shall

we let's not ruin it for them hang on a second somebody talk to me i need feedback what

happened who i just need a human to talk to come on that's a good joke give me a human to talk to janine I

know it's not your fault but talk to me we got We just got a two minutes left i'm right here they're re-rackcking it

they're re-rackcking it i don't even know what that means okay [Music]

[Applause] [Music] what did you do

[Music] tell me that wasn't amazing hey

Blue how are you doing how do you like How do you like your new physics engine you like it huh

yeah I bet i know tactical feedback rigid body soft body simulation super real

time can you imagine just now what you were looking at is comp complete real time

simulation this is how we're going to train robots in the future uh just so you know Blue has uh

two computers two Nvidia computers inside look how smart you are yes you're smart okay

all right hey Blue listen how about let's take them home let's finish this keynote it's

lunchtime are you ready let's finish it up we have another announcement to You're good you're good just stand right

here stand right here stand right here all right good right there that's good all right stand

[Applause] okay we have another amazing news i told you the progress of our

robotics has been making enormous progress and today we're announcing that Groot

N1 is open sourced [Applause] i want to thank all of you to come

to let's wrap up i want to thank all of you for coming to GTC we talked about several things one Blackwell is in full

production and the ramp is incredible customer demand is incredible and for good reason because there's an

inflection point in AI the amount of computation we have to do in AI is so much greater as a result of reasoning AI

and the training of reasoning AI systems and agent agentic systems second Blackwell MVLink 72 with Dynamo is 40

times the performance AI factory performance of Hopper and inference is going to be one of the most important

workloads in the next decade as we scale out AI third we have an annual annual rhythm of road maps that

has been laid out for you so that you could plan your AI infrastructure and then we have two we have three AI

infrastructures we're building ai infrastructure for the cloud AI infrastructure for enterprise and AI

infrastructure for robots [Music]