Episode 4: Skills and Smarts – What GR00T N1 Can Do

Welcome back to our GR00T N1 deep dive. So far, we’ve covered the “what” and the “how” – what GR00T N1 is made of and how it was trained. Now it’s time for the exciting part: What can GR00T N1 actually do? In this episode, we’ll explore the capabilities of this model and why they’re a big leap forward for robotics. We’ll talk about the tasks it can perform, how well it performs them, and how it stacks up against previous methods.

The ultimate goal of GR00T N1 is to give robots a broad set of generalized skills. Straight out of training, without heavy specialization, GR00T N1 can tackle a range of common manipulation tasks. These include things like:

• Grasping objects: The model can control a robot to reach out and grasp items, whether it’s picking up a tool, a toy, or a package. Importantly, it can handle both single-handed grasps or using two hands together for larger objects.
• Moving and placing objects: Once it picks something up, it can move it to a desired location. This could be as simple as moving a box from the floor to a shelf, or as involved as rearranging objects on a table following instructions.
• Hand-to-hand transfers: GR00T N1 even learned behaviors like passing an object from one hand to the other. Imagine a humanoid robot that picks up a can with its left hand, then transfers it to the right hand to place it on a higher shelf – that kind of coordinated bimanual action is within the model’s repertoire.
• Multi-step tasks: Because of the “thinker” part of the model, it can plan multiple steps in sequence. So if you tell the robot, “open the cabinet, then take out the bowl and put it on the counter,” GR00T N1 can break that down: open door (one action sequence), reach for bowl (next sequence), place bowl (next). It keeps the context of the overall goal so it can chain these skills together in the right order.

What’s truly impressive is that GR00T N1 can generalize these skills to new combinations and contexts. It wasn’t explicitly pre-programmed for each exact scenario. Instead, because it has seen so many variations during training, it can adapt on the fly. For example, it learned the concept of “grasping” in general, so it can apply it to objects it hasn’t seen before, within reason. Or it understands the notion of left vs right hand, so it can decide to switch hands if a task would be easier that way.

Now, how well does it do these things? NVIDIA and researchers put GR00T N1 through a battery of tests, both in simulations and in some real-world trials. In simulation benchmarks, GR00T N1 outperformed previous state-of-the-art models that were trained for each specific task (like imitation learning models specialized to certain environments). For instance, on standard robotic test tasks (such as stacking blocks or navigating a simple obstacle course to reach an object), GR00T N1 achieved higher success rates than models that didn’t have its benefit of broad training. This is remarkable because those specialized models had an advantage: they were tuned just for that task, whereas GR00T N1 was more of a generalist. Yet, the foundation model’s massive training gave it an edge, demonstrating the power of breadth of knowledge.

One key area of evaluation was multiple robot embodiments. GR00T N1 was tested on controlling different kinds of robots – not just one specific humanoid. In simulations, they tried its brain on, say, a dexterous two-armed system and also on a different bipedal robot, and possibly even wheeled robots. The results showed that GR00T N1 could adapt with minimal adjustment, whereas traditionally you’d need to train a new model from scratch for each robot. This cross-embodiment skill is a game changer: it hints that we could have a single intelligent model that powers many kinds of robots in the future, much like one operating system can run on different hardware.

Beyond simulation, let’s talk about real-world demonstrations, because that’s the true test. One milestone was deploying GR00T N1 on a real humanoid robot known as the Fourier GR-1. The GR-1 is a human-sized, bipedal robot developed by a company called Fourier Intelligence. Using GR00T N1 as its brain, the GR-1 was tasked with some language-conditioned bimanual manipulation tasks – essentially, the robot was given verbal instructions to use both its hands to do something, like “pick up the two objects and put them together” kind of tasks. The outcome? The robot performed impressively well, completing the tasks with high success rates. Even more striking was the data efficiency observed – the team didn’t need to collect months of new data on the real robot to make this work. They did a light fine-tuning with a small amount of robot-specific data, and GR00T N1 was able to generalize its learned skills to this actual machine’s body. Achieving fluent bimanual action on a physical humanoid is a big step forward, since coordinating two arms, vision, and instructions all at once is very challenging for robots.

Another eye-catching example was a demonstration by a startup called 1X Technologies. 1X is a company building humanoid robots for real-world tasks (their robot model is named NEO). In a live demo, they showed their humanoid robot autonomously tidying up a room – picking up clutter, arranging items – guided by a policy built on GR00T N1. The important detail is that 1X didn’t have to develop a complex algorithm from scratch for this; they took NVIDIA’s pre-trained GR00T N1, gave it some additional training specific to their robot and the tidying tasks, and the robot was able to perform the job. The CEO of 1X commented on how this approach accelerated their progress toward making robots that are not just rigid tools, but adaptable companions that can assist in meaningful ways. In other words, GR00T N1 provided a huge head-start, offering general reasoning and skills that they only had to refine a little for the specific scenario.

Let’s not forget, GR00T N1 is open. This means many others got to experiment with it early on. Robotics leaders like Agility Robotics (known for their bipedal robot Digit) and Boston Dynamics (makers of the famous Atlas and Spot robots) were among the early access users. While we don’t have public demos from them using GR00T N1 yet, the fact that such companies are exploring it speaks volumes. They see value in a generalist AI brain that could potentially be plugged into their advanced robot bodies. It’s like giving their robots a ready-made education instead of having to teach from scratch.

Now, you might be wondering, does GR00T N1 simply do everything perfectly? Of course not – it’s a huge step forward, but it’s not magic. There are limits. It might struggle if asked to do something way outside its training distribution or something requiring extremely fine manipulation it hasn’t practiced (like threading a needle, unless it saw that in training). And while it can generalize, it’s still helpful to give it some fine-tuning on the exact robot model for optimal performance. But compared to previous gen robot brains, which would often completely fail if anything was even slightly different from what they were trained on, GR00T N1 is far more robust.

To give a sense of the performance improvements in measurable terms: in some language-following tasks in the lab, GR00T N1 had significantly higher success rates than older models. For example, if a test was “the robot hears ‘pick up the red block and place it on the blue block’ and has to do it,” GR00T N1 might succeed most of the time whereas a baseline might only succeed half the time. And remember, GR00T N1 is just the first version – hint, hint: an improved version called N1.5 came out shortly after and did even better, which we’ll discuss soon.

In summary, GR00T N1 endowed robots with a suite of useful skills out-of-the-box: grasping, moving objects, using both hands, following spoken or written instructions to carry out multi-step tasks, and adapting those skills to new situations. It raised the bar for what we can expect a single AI model to do in the realm of robotics. Perhaps the best testament to its capability is the excitement and adoption from the robotics community. We’re seeing robots that traditionally would have required months of programming now do fairly complex tasks after just a short fine-tuning with this model. That’s a huge win for efficiency and opens the door to faster development of robotic solutions in many areas – from factories (where tasks might include sorting items, packing goods) to home robotics (imagine a robot helper that can clean up various household messes without being explicitly hard-coded for each one).

Coming up in our next episode, we’ll talk more about those real-world applications and how GR00T N1 is being put to use by companies and research groups. We touched on a few examples here, but we’ll dive a bit deeper into how different organizations are leveraging this model, and what tools NVIDIA is providing to support this ecosystem (like simulation blueprints and updates to the model). This will give us a sense of the momentum building around GR00T N1 and generalist robot intelligence. Stay tuned for some concrete use cases and forward-looking insights!

(Outro:) That’s it for the capabilities overview. We’ve seen that GR00T N1 can handle a surprisingly wide array of tasks for a single model, and do so across different robots and environments. In Episode 5, we’ll focus on real robots and real results – essentially, case studies of GR00T N1 in action beyond the lab. Don’t miss it if you want to know how this is actually hitting the ground (or factory floor, or home) with cutting-edge robots today.