Welcome back to our deep dive on NVIDIA’s GR00T N1. In the last episode, we talked about how this model is ushering in a new era of generalist robots. Now it’s time to get technical and explore what’s inside GR00T N1’s “brain.” Don’t worry – we’ll keep it conversational. The architecture of GR00T N1 is one of the most fascinating aspects because it draws inspiration from the way we humans think and act. NVIDIA describes it as a dual-system architecture, and a great way to think about it is by comparing it to two modes of human cognition: a fast, intuitive side and a slow, reasoning side.

Let’s break it down. GR00T N1 is a VLA model – that stands for Vision-Language-Action. In essence, it combines three key abilities:

What makes GR00T N1 truly special is how it processes these three aspects in a coordinated way. This is where the dual-system architecture comes into play. The model essentially has two major components working hand-in-hand, which NVIDIA has playfully nicknamed System 2 and System 1 – a nod to psychological theories of human thinking (often called System 2 for slow thinking and System 1 for fast thinking).

Crucially, these two systems aren’t separate AI minds – they’re trained together, as one unified model, so that they complement each other. System 2 gives context and guidance to System 1, and System 1’s capabilities feedback into what System 2 can expect. For example, System 2 might output a plan like “approach the table, then extend right arm to grab the mug.” System 1 receives that in the form of an embedding or set of parameters and then handles the nitty-gritty of executing those steps in real time. If something is off – say the mug isn’t exactly where expected or starts to slip – System 1 can adjust on the fly, and System 2 can also re-evaluate if needed. It’s a tight coupling, much like how your intuitive actions and conscious thoughts work together seamlessly when you perform tasks.

This architecture – having a reasoner and a doer – is novel in robotics at this scale. In the past, a robot might have had separate modules (vision module, planning module, control module) coded separately and interacting in rigid ways. GR00T N1 instead learns a holistic policy: from camera pixels and language input all the way to motor torques, in one model. The “brain” of the robot, so to speak, has both a high-level cortex and a low-level muscle memory baked in.

To put it in perspective, System 2 in GR00T N1 is like the part of a human brain that figures out what to do (slow but smart), and System 1 is like the spinal cord and cerebellum that handle rapid movements (fast and fine-tuned). For instance, if you decide to catch a falling object, you intellectually know you should catch it (System 2 reasoning) but your actual motion to grab it is almost instinctual (System 1 reflex). GR00T N1’s design allows it to have a bit of that two-tiered intelligence.

So how is System 2 implemented? It uses a large neural network that can understand images and text together. NVIDIA’s internal code name “Eagle” was mentioned – presumably a sophisticated vision-language model with billions of parameters that’s been trained on a lot of visual and textual data. System 1, the diffusion transformer, is another neural network that takes in the state of the robot (its joint positions, etc.), some “noise” (as part of the diffusion process), and also gets to look at System 2’s output (via a mechanism like cross-attention, meaning it can focus on relevant parts of the instruction or visual context). It then predicts the sequence of motor commands needed. The “diffusion” part means that System 1 doesn’t simply predict one next action – it’s been trained to generate whole trajectories by refining noisy action sequences into correct ones. This approach helps in exploring multiple possible action paths and settling on one that achieves the goal smoothly.

All of this might sound very technical, but the takeaway is: GR00T N1’s brain is both broad and deep. Broad, because it handles multimodal input (vision + language) and outputs actions. Deep, because it has specialized sub-systems for understanding and execution, working in tandem. This dual-system design is a key reason why GR00T N1 can handle complex tasks in more human-like ways. It’s not just reacting with hard-coded responses; it’s thinking through the task and then physically acting it out, all in real time.

Now that we’ve looked at the inner workings of GR00T N1’s architecture, you might be wondering: how do you actually train such a complex brain? What does it take to teach a model to see, understand, and do all these things? In our next episode, we’ll talk about the immense training process behind GR00T N1 – the “education” that this AI brain underwent. We’ll see how NVIDIA taught it using everything from robot demonstrations to simulated worlds and even internet videos. Stay tuned, because the training story is just as fascinating as the design itself!

(Outro:) That’s it for today’s technical tour inside GR00T N1. I hope the dual-system concept is clearer now – it’s a powerful idea marrying thoughtful planning with split-second action. In Episode 3, we’ll dive into how this model was trained and the kinds of data that made it as smart as it is. Don’t go away – the learning journey of GR00T N1 is coming up next!