Episode 17: The Role of Foundation Models in Sim2Real

Shaoqing Tan
Thu, 31 Jul 2025 07:04:00 GMT

Welcome to the final episode of our series on the Sim2Real challenge. We've been on a long journey, from dissecting the "reality gap" to exploring techniques for building robust and adaptable robots. We've learned how to embrace chaos with Domain Randomization, how to train against adversaries, and how to create policies that can learn and adapt on the fly.

But today, we're looking at a new frontier, a paradigm shift in AI that is poised to revolutionize robotics: Foundation Models. This is not just about making our robots more robust or more adaptable; it's about giving them a dose of something they've always lacked: common sense.

So, what are these "foundation models"? You've probably heard of them, even if you don't recognize the name. Models like GPT-3 and DALL-E are examples of foundation models. They are massive neural networks, trained on vast swathes of the internet—text, images, videos, and code. This immense training data allows them to acquire a general, high-level understanding of the world.

In robotics, these models are a game-changer. For the first time, we can take a model that has learned about the world from the internet and use that knowledge to control a robot.

Let's start with perception. A robot's ability to act is limited by its ability to see and understand its environment. This is where a model like Meta AI's Segment Anything Model (SAM) comes in. SAM is a vision foundation model that can segment, or outline, any object in an image, even if it has never seen that object before. This is a huge leap forward from traditional computer vision models, which can only recognize a fixed set of objects. With SAM, a robot can perceive its environment with a level of detail and granularity that was previously unimaginable. It can see a cluttered table and understand that it's covered in individual, distinct objects.

But what if we could go beyond just seeing the world? What if our robots could understand our language and connect it to what they see? This is the promise of Vision-Language-Action models, or VLAs.

The state of the art here is Google's Robotic Transformer 2, or RT-2. RT-2 is a single model that takes in a robot's camera feed and a natural language command, and directly outputs the actions the robot should take.

The truly remarkable thing about RT-2 is that it leverages knowledge from the web to understand abstract concepts. For example, you could tell RT-2 to "pick up the trash," and it would be able to identify which of the objects on a table is trash, even if it has never been explicitly trained on that specific type of trash before. It has learned the concept of trash from the internet.

RT-2 has also demonstrated "emergent skills" and "chain-of-thought reasoning." It can perform tasks that it wasn't explicitly trained on, and it can break down complex, multi-step commands into a sequence of simpler actions. This is a major step towards robots that can follow complex instructions and reason about the world in a more human-like way.

The rise of foundation models is leading to a new paradigm in robotics. We're moving away from training a single, end-to-end policy from scratch. Instead, we're seeing the emergence of hybrid systems, where a large foundation model provides the "brains" of the operation—the high-level perception and reasoning—and a smaller, more specialized policy provides the "muscle"—the low-level motor control.

The journey from simulation to reality is far from over. There are still many challenges to overcome. We need more robotics-specific data, and we need to ensure that these powerful models are safe and reliable. But the future of robotics has never been brighter.

Foundation models are giving us a glimpse of a world where robots are not just tools, but true partners, able to understand our language, our intentions, and our world. The dream of a general-purpose robot, one that can learn and adapt and help us in our daily lives, is no longer a distant vision, but a rapidly approaching reality.